UBC Theses and Dissertations

UBC Theses Logo

UBC Theses and Dissertations

Production planning in JS McMillan Fisheries Ltd. : catch allocation decision support tool design Begen, Mehmet Atilla 2001

Your browser doesn't seem to have a PDF viewer, please download the PDF to view this item.

Item Metadata

Download

Media
831-ubc_2002-0018.pdf [ 15.34MB ]
Metadata
JSON: 831-1.0090086.json
JSON-LD: 831-1.0090086-ld.json
RDF/XML (Pretty): 831-1.0090086-rdf.xml
RDF/JSON: 831-1.0090086-rdf.json
Turtle: 831-1.0090086-turtle.txt
N-Triples: 831-1.0090086-rdf-ntriples.txt
Original Record: 831-1.0090086-source.json
Full Text
831-1.0090086-fulltext.txt
Citation
831-1.0090086.ris

Full Text

P R O D U C T I O N P L A N N I N G i n JS M c M I L L A N F I S H E R I E S L T D : C A T C H A L L O C A T I O N DECISION SUPPORT T O O L  DESIGN  Mehmet Atilla Begpn BSc (Industrial Engineering), Middle East Technical University, Turkey, 200  A THESIS S U B M I T T E D I N PARTIAL F U L F I L L M E N T O F T H E R E Q U I R E M E N T S F O RT H E D E G R E E O F  M A S T E R O F S C I E N C E (Business A c b i i n i s t r a t i o n )  THE FACULTY O FGRADUATE  STUDIES  (Faculty o f C o m m e r c e & Business A d m i n i s t r a t i o n )  W e accept this thesis as c o n f o n n i n g „ to the required standard  T H E UNIVERSITY O F BRITISH C O L U M B I A D e c e m b e r 2001  ©MehmetAtillaBegn,2001  In  presenting  degree freely  at  the  available  copying  of  department publication  this  of  in  partial  fulfilment  University  of  British  Columbia,  for  this or  thesis  reference  thesis by  this  for  his thesis  and  scholarly  or for  her  of  The University of British Columbia Vancouver, Canada  DE-6  (2/88)  I  I further  purposes  gain  the  shall  requirements  agree  that  agree  may  representatives.  financial  permission.  Department  study.  of  be  It not  is be  that  the  for  Library  an shall  permission for  granted  by  understood allowed  the  advanced make  extensive  head  that  without  it  of  copying my  my or  written  ABSTRACT JS McMillan Fisheries Ltd. (JSM) is a Vancouver-based company with operations in nearly all levels of the commercial fishing industry, from supply through distribution. The heart of the operation is the processing facilities where freshly caught Pacific salmon are prepared for sale to end consumers and institutional buyers.  As the  operations of JSM evolved, the decision making for allocating a catch of salmon with varying characteristics amongst a set of final products has become too complex and time consuming.  The focus of this study is to determine an effective and efficient method for JSM to allocate daily a fresh salmon harvest between the various products they produce on a daily basis. The goal is short-term  production planning, to allocate the catch  among the products in such a manner that the profit potential of the catch is maximized, i.e. prepare a production schedule that maximizes the total profit over the planning horizon. Additional goals of this project include:  automation of the  decision making process for the catch allocation, "what if" planning, decreasing expert dependency, reducing decision making time, and building a practical and innovative decision support tool.  In order to solve this problem efficiently and effectively, optimization models were developed for allocating the catch to the end products. A corresponding decision support tool was built for the end-users at JSM.  Key words; fish processing planning, fish deterioration modeling, by-products modeling, decision support tool for catch allocation, software development in Excel, salmon, optimal catch allocation, production planning, linear, stochastic, integer programming.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  ii  TABLE OF CONTENTS ABSTRACT  ii  TABLE OF CONTENTS  iii  LIST OF THE FIGURES  v  LIST OF THE TABLES  vi  ACKNOWLEDGMENT  vii  1. INTRODUCTION 1.1 PROJECT BACKGROUND  1 1  1.1.1 Project Description and Problem Definition  1  1.1.2 Fishing Industry in BC: Salmon Catching Methods and Species  5  1.1.3 JS McMillan Fisheries Ltd.  9  1.2 SUMMARY OF PREVIOUS WORK  14  1.3 LITERATURE REVIEW  15  2 INITIAL APPROACH 2.1 SPLP - SINGLE PERIOD LINEAR PROGRAM  18 18  2.1.1 SPLP Model  20  2.1.2 Catch allocation tool -CAT.  24  3. REVISED CATCH ALLOCATION PROBLEM 3.1 MPMSLP MULTI PERIOD MULTI SPECIES LINEAR PROGRAM  3.1.2 MPMSLP Model  26 26  29  3.2 EXTENSIONS TO M P M S L P  33  3.3 D A T A REQUIREMENTS OF THE MODEL  35  3.4 A DECISION SUPPORT TOOL FOR M P M S L P  35  3.5 PRACTICAL ISSUES  37  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  4. RESULTS AND FINDINGS  40  4.1 IMPLEMENTATION  40  4.2 SAMPLE OUTPUT  41  4.2.1 Sample Output with mostly negative profit coefficients 4.2.2 Sample Output with all positive profit coefficients 5. EXTENSIONS  41 43 45  5.1 STOCHASTIC SUPPLY  45  5.2 CATCH SIZE FORECASTING  45  5.2.1 Models  47  5.2.2 Results  48  5.3 STOCHASTIC OPTIMIZATION  5.3.1 Stochastic LP formulation 5.4 A COMBINED PRODUCTION PLAN FOR BOTH PLANTS  5.4.1 MIP formulation 5.5 FURTHER RESEARCH  49  49 52  53 55  6. CONCLUSION  56  REFERENCES  57  A P P E N D I X 1- S U M M A R Y O F T H E P R O D U C T I O N P R O C E S S  58  A P P E N D I X 2 - FISH C A T E G O R I E S A N D P R O D U C T LIST F O R C H U M S  59  APPENDIX 3 - PRINCE RUPERT'S P L A N T HIGHLIGHTS  60  APPENDIX 4 - A M P L M O D E L  64  APPENDIX 5 - C A T DESCRIPTION  65  APPENDIX 6 - MPMSLP PROTOTYPE  77  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  LIST OF THE FIGURES FIGURE 1 - TROLLING  6  FIGURE 2 - SEINING  6  FIGURE 3 - GILLNETTING  6  FIGURE 4 - C H U M SALMON  7  FIGURE 5 - SOCKEYE SALMON  7  FIGURE 6 - PINK SALMON  8  FIGURE 7 - S P L P GRAPHICAL REPRESENTATION  19  FIGURE 8 - M P M S L P GRAPHICAL REPRESENTATION  28  FIGURE 9 - DETERIORATION REPRESENTATION  28  FIGURE 10 - T H E LOGIC AND FLOW DIAGRAM OF C A T 2  36  FIGURE 1 1 - DAILY LANDED C A T C H SUMMER 2 0 0 0  46  FIGURE 12 - PLOTS FOR TOTAL LANDINGS, MEAN, NAIVE AND M A (2)  49  FIGURE 13 - STOCHASTIC L P SCENARIOS  50  FIGURE 14 - COMBINED PRODUCTION P L A N FOR VANCOUVER AND PRINCE RUPERT  52  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  LIST OF THE TABLES T A B L E 1 - 1999 C A T C H VOLUME IN LBS, LANDED VALUE, AND WHOLESALE VALUE  5  TABLE 2 - FISH QUALITY DETERIORATION MATRIX  31  T A B L E 3 - SAMPLE INPUTS TO THE S P L P M O D E L  41  T A B L E 4 - PRODUCTS THAT HAVE POSITIVE PROFITS  42  T A B L E 5 - SAMPLE OUTPUT FOR THE S P L P WITH CURRENT PROFIT COEFFICIENTS  42  T A B L E 6 - SAMPLE OUTPUT FOR THE S P L P WITH ALL POSITIVE PROFIT COEFFICIENTS  44  T A B L E 7 - SUMMARY T A B L E OF FORECASTING RESULTS  48  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  A CKNO WLED GMENT C o m i n g to C a n a d a w a s o n e of t h e b i g g e s t d e c i s i o n s t h a t I h a v e m a d e in m y life. I a m happy with my decision and I believe that my choice was correct. Living, studying a n d w o r k i n g in C a n a d a has b e e n a v e r y p r a c t i c a l a n d fulfilling e x p e r i e n c e for m e . I really e n j o y e d t h e m a s t e r ' s p r o g r a m , m y p r o j e c t a n d V a n c o u v e r .  I h a v e l e a r n e d a n d b e n e f i t e d a lot f r o m t h e C O E M S c p r o g r a m . T h i s p r o g r a m a l l o w e d m e to g a i n w o r k i n g e x p e r i e n c e in a real life p r o b l e m w h i l e s t u d y i n g . A n d t h e C O E ' s f i n a n c i a l a i d d u r i n g m y s t u d i e s is g r e a t l y a p p r e c i a t e d . I w o u l d like to t h a n k all t h e people who welcomed me w a r m l y and gave support during m y studies.  I s t a r t w i t h m y p a r e n t s a n d f a m i l y w h o e n c o u r a g e d m e to g o to a b r o a d f o r m y m a s t e r d e g r e e a n d s u p p o r t e d m e d u r i n g all m y life. I o w e t h e m a lot a n d w o u l d not be h e r e w i t h o u t t h e m .  I w o u l d like to t h a n k Dr. M a r t i n P u t e r m a n w h o g a v e his g r e a t e n c o u r a g e m e n t a n d support during my studies also who encouraged and persuaded  m e to c o m e  to  C a n a d a a n d C O E p r o g r a m . I w o u l d not be here w i t h o u t h i m . I a l s o w o u l d like to t h a n k to Dr. D a v i d G l e n n for his c o n t r i b u t i o n s , a d v i c e a n d s u p p o r t f o r t h e p r o j e c t a n d t h i s t h e s i s . T h e i r c o n t r i b u t i o n s to t h i s p r o j e c t a n d t h e s i s are e n o r m o u s .  I a l s o w o u l d like to t h a n k S t e p h e n J o n e s , Paul H i o m , L a u r e n G r a y a n d all t h e p e o p l e in C O E for t h e i r s u p p o r t d u r i n g t h e p r o j e c t a n d m y s t u d i e s . A l s o m a n y t h a n k s to all m y c l a s s m a t e s a n d f r i e n d s w h o m a d e t h i s s t u d y f u n e x p e r i e n c e a n d h e l p e d m e out whenever  I  needed.  Special  thanks  to  Omar  Ladak  for  his  assistance  in  the  forecasting project.  A n d finally I w o u l d Bodmer,  Brain  like to t h a n k B a r r y a n d  Gilley  and  Phil  Young  from  D a n M c M i l l a n , S t e v e P a r k h i l l , Paulli JS  McMillan  for  providing  such  a  c h a l l e n g i n g a n d i n t e r e s t i n g p r o j e c t o p p o r t u n i t y a n d t h e i r t r u s t a n d s u p p o r t for t h e project.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  vii  1.  INTRODUCTION  1.1 PROJECT BACKGROUND 1.1.1 Project Description This thesis describes the  and Problem work  Definition  done for  the  project  entitled  "Optimal  Catch  Allocation" for JS McMillan Fisheries Ltd. (JSM) with the Centre for Operations Excellence (COE) at the University of British Columbia (UBC).  The work presented in this thesis is the continuation of a study with JSM and COE done by a previous graduate student in COE. The project started July 2000 after JSM approached COE with the catch allocation problem and I have been working on this project  since September 2000. The previous work  will  be summarized in the  "summary of previous work section" in this chapter.  JS McMillan Fisheries Ltd. is a Vancouver-based company with operations in nearly all levels of the commercial fishing industry, from supply through distribution. The heart of the operations is the processing facilities where freshly caught Pacific salmon are prepared for sale to end consumers and institutional buyers. Decision making for allocating a catch of salmon with varying characteristics amongst a set of final products became too complex and time consuming as the operations of JS McMillan Fisheries Ltd evolved, and the  numbers of products,  markets and  operations  increased.  The focus of this project is short-term production planning. That is to develop an efficient method for JS McMillan to allocate a fresh salmon harvest between the various salmon products that they prepare, such as dressed salmon, filleted salmon, and canned salmon. The goal is to allocate the catch among the products in such a manner that the profit potential of the catch is maximized each day. The main goal for this project is to develop and implement a decision support tool that allocates the catch optimally.  In order to solve the problem optimally the characteristics of the catch should be clearly understood. Salmon processing is seasonal and the department of fisheries  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  1  d e c i d e s w h e n a n d w h e r e s a l m o n will be c a u g h t . T h e s a l m o n s p a w n c y c l e is u s u a l l y 2-4  y e a r s . F i s h e r m e n c a n o n l y c a t c h t h e s a l m o n d u r i n g s p e c i f i c t i m e s , in s p e c i f i c  p l a c e s . For e x a m p l e t h e d e p a r t m e n t  of f i s h e r i e s o n l y a l l o w s f i s h i n g , b e f o r e  the  s a l m o n can e n t e r to river w h e r e t h e y s p a w n a n d d i e . F i s h e r m e n a r e notified a b o u t t h e t i m e , d u r a t i o n a n d place of t h e c a t c h by t h e d e p a r t m e n t of f i s h e r i e s . T h i s is c a l l e d t h e c a t c h o p e n i n g a n n o u n c e m e n t . O n e of t h e r e a s o n s w h y d e p a r t m e n t  of  f i s h e r i e s has s u c h a r e g u l a t i o n is t h a t it w a n t s to p r o t e c t t h e fish s u p p l y so t h a t e n o u g h fish c a n s p a w n t h e n e x t g e n e r a t i o n . T h e r e a r e d i f f e r e n t p a r t i e s i n v o l v e d in this  business  commercial  such  fishing  as  First  boats.  Nations  Seasonal  People, salmon  recreational runs  and  fishermen  department  and of  other  fisheries  r e g u l a t i o n c a u s e s a l m o n f i s h i n g to be d o n e in s h o r t t i m e p e r i o d s . B u t w i t h  this  s i m u l t a n e o u s f i s h i n g , all b o a t s a n d fish p r o c e s s i n g p l a n t s h a v e s a l m o n at t h e s a m e t i m e . U s u a l l y , J S M g e t s m o r e fish t h a n it c a n p r o c e s s d u r i n g a s h o r t period of t i m e . W h i l e I w a s v i s i t i n g t h e Prince R u p e r t plant, in o n e d a y 6 0 0 , 0 0 0 lbs of s a l m o n , m o r e than  twice  of t h e  capacity,  was  landed.  According  to J S M , t h i s  number  can  s o m e t i m e s be m o r e t h a n o n e m i l l i o n p o u n d s . T h e r e a r e a l s o s o m e u n c e r t a i n t i e s s u c h a s c a t c h s i z e s a n d d u r a t i o n of c a t c h o p e n i n g s . T h e q u a n t i t y a n d c h a r a c t e r i s t i c s ( s i z e , s p e c i e , q u a l i t y ) of t h e c a t c h c o m i n g to t h e plant m a y n o t be k n o w n until t h e f i s h i n g b o a t a r r i v e s at t h e p l a n t .  F u r t h e r m o r e , t h e d e p a r t m e n t of f i s h e r i e s c o u l d tell t h e  f i s h i n g b o a t s t h a t t h e y c a n c o n t i n u e f i s h i n g for s o m e m o r e t i m e s a y , t h r e e m o r e d a y s but a r e not a l l o w e d to c a t c h a s p e c i f i c s p e c i e s , s a y pink. If d e p a r t m e n t of f i s h e r i e s d e c i d e s t h a t p i n k s u p p l y is s t r a i n e d , t h e y c l o s e t h e f i s h i n g .  D e p a r t m e n t of f i s h e r i e s policy, u n c e r t a i n t i e s in c a t c h a m o u n t , s e a s o n a l i t y , a n d s h o r t t i m e p e r i o d s for f i s h i n g m a k e p r o d u c t i o n p l a n n i n g a c o m p l i c a t e d t a s k t h a t h a s to be s o l v e d v e r y q u i c k l y . T i m e is a n issue b e c a u s e t h e r e c a n be a n o t h e r c a t c h c o m i n g n e x t d a y a n d s a l m o n m u s t be p r o c e s s e d a s q u i c k l y as p o s s i b l e to d e c r e a s e t h e f i s h d e t e r i o r a t i o n . A t t h e s a m e t i m e , f i n d i n g a g o o d s o l u t i o n m i g h t be difficult in t h a t e n v i r o n m e n t since the  catch  amount,  characteristics change  from  one  catch  to  a n o t h e r a n d past e x p e r i e n c e m a y not h e l p . H e n c e , f i s h p r o c e s s i n g p l a n t s a n d J S M n e e d a n efficient a n d e f f e c t i v e , w a y to f i g u r e o u t w h a t to d o e a c h d a y .  O v e r t h e c o m p a n y ' s h i s t o r y , t h e d e c i s i o n of a l l o c a t i n g t h e c a t c h to t h e e n d p r o d u c t s (i.e. m a k i n g a f e a s i b l e p r o d u c t i o n  plan) has b e e n c o m b i n e d  experience,  market  e x p e c t a t i o n s a n d i n t u i t i o n . H o w e v e r , as t h e c o m p a n y has e v o l v e d t h e n u m b e r of  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  2  products, fish types and decision-making parameters have increased and thus the decision-making has become a time consuming and complicated process. The owners are involved heavily in this decision-making process and they are looking forward to being less involved. They would like to transfer their responsibilities to managers who will be assisted by a decision support tool.  On the other hand developing only a feasible production plan is not the best thing to do. There is an also need for optimal decisions in order to increase the company's profitability.  Moreover,  time  definitely  is  an  issue  due  to  salmon  business  characteristics: fish is a perishable product and the salmon supply is seasonal. We hope that the decision support tool developed in this project will overcome these difficulties and provide the optimal production plan for allocating the catch to the end products to JSM very efficiently.  The scope of the project is to "Build an automated, customizable decision support tool that will allow the user to enter decision-making parameters, produce a feasible production schedule that maximizes the expected profit of the total catch and give the production schedule as output, record the past data".  The specific goals for the project are: Producing a feasible production schedule that maximizes the profit:  Optimization model will allocate the catch to end products in such a way that the profit from these products will be maximized and this allocation will be feasible for the plant, products and catch properties. For example for plant; each product line has a certain capacity, for products; it is not possible to produce a certain product from every type of fish and for catch; what and how much to produce depend on the catch amount and its characteristics.  Automating the decision making process:  Due to dynamic characteristics of salmon fishing the catch allocation decision must be made each day and some automated and standardized way for decision-making is crucial. An optimization model itself is not enough for practical use of end users. For this reason the decision support tool (used as Catch Allocation Tool as well, CAT), which uses optimization,  may be developed to provide easy and  user-friendly  software for the end users. This software will be used on a daily basis by decision makers.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  3  CAT has a user interface, which will enable the user to enter the necessary inputs and provide the optimal catch allocation as output and automate the decision making process.  Decreasing decision making time:  Sometimes there is a very short time between when the company receives the catch information and when they start production. Moreover, the current decision-making process is done by hand and is costly in terms of time. In one of meetings it was mentioned that it could take a couple of hours to produce a production plan. A Decision Support Tool will decrease the decision-making time considerably  by  automating it. As an example, the catch allocation decision can be received in a minute after entering all the inputs to the CAT.  Decreasing expert dependency:  The catch allocation tool has been developed according to current decision-making processes and decision makers' practices so that it will  decrease the  expert  dependency by using and automating the expert knowledge and experience in the decision-making process. Described above, CAT will enable the user, not necessarily the decision maker, to enter the inputs for decision-making and give the catch allocation as output. Decision makers can check the tool's recommendations for catch allocation then can make some adjustments and solve it again. Moreover they can analyze many different scenarios and see how the production schedule changes. Thus CAT uses and automates the expert knowledge and experience in the decisionmaking process and reduces expert dependency.  The thesis organization is as follows: The project background, project description, company and fishing industry information, previous work and literature review can be found in this (first) chapter. The second chapter discusses the initial approach to the catch allocation problem. The revised problem description and approach are included in the third chapter. The thesis concludes with findings in chapter four and extensions in chapter five.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  4  1.1.2 Fishing Industry in BC: Salmon Catching Methods and Species The information in this section is mostly taken from taken from Fisheries and Oceans and BC fisheries web sites. Please refer to http://www.ncr.dfo.ca/index.htm  and  http://www.qov.bc.ca/fish for more information.  The marine and fresh waters of British Columbia provide more than 80 commercially harvested or farmed species. The seafood industry employs more than 6,800 people in full and part-time positions including fish harvesting, vending, buying, brokering, aquaculture and fish processing. British Columbia shipped a total $850 million worth of seafood to more than 50 countries during 1999. Commercial fishing is the fourth largest primary industry in British Columbia after forestry, mining and agriculture.  The 1999 season figures are presented in Table 1.  Species  Landings (lbs)  Landed Value ($million)  Wholesale Value ($million)  Wild Salmon  37,300,000  25.4  169  Herring  65,700,000  51.5  119.3  Ground fish  318,600,000  100.1  163.8  38,000,000  93.5  141  Other  4,500,000  2  5.7  Total  464,000,000  311.2  657.3  Wild Shellfish  Table 1 - 1999 Catch volume in lbs, landed value, and wholesale value.  Both BC and Alaska regions are very rich in terms of fish supply and JSM acquires its fish supply from both regions. Although JSM processes some ground fish besides salmon, the project focus is on salmon only. Specifically, chum, pink, and sockeye salmon species are considered.  Like salmon, herring fishing is seasonal whereas ground fish can be caught any time with some quota restrictions. Fishing boats can fish for these species any time they want as long as their total catch for that year is less than the quota amount. When this is the case for ground fish, JSM has authority over fishing boats and can require them to bring a certain amount on at specified times so that JSM can handle their  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  5  production planning more easily. Despite the fact that the other species are out of this project scope, there is an opportunity for further study with these species in the future.  Salmon are usually caught in three different ways depending on the fishing boat type. These are trolling, seining and gillnetting. Figure 1 - Trolling Trailers employ hooks and lines, which are suspended from large poles extending from the fishing  vessel.  arrangement  of  Altering  the  lures used on  type  and  lines allows  various species to be targeted. Trailers catch approximately 2 5 % of the commercial harvest in BC.  Figure 2 - Seining Seine nets are set from fishing boats with the assistance of a small skiff. Nets are set in a circle around aggregations of fish. The bottom edges of the net are then drawn together into a "purse" to prevent escape of the fish. Seiners take approximately  5 0 % of the  commercial  catch.  Figure 3 - Gillnetting Salmon gill nets are rectangular nets that hang in the water and are set from either the stern •  or bow of the vessel. Fish swim headfirst into the net, entangling their gills in the mesh. Altering mesh size and the way in which nets are suspended in the water allows nets to target selectively on certain species and sizes of fish. Gill-netters take about 2 5 % of the commercial catch.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  6  The focus of the project is chum, pink and sockeye species.  Figure 4 - Chum Salmon Silver Streaks No spots  Chum Salmon Larqe Pupi  Large Mouth (Maxillary extends behind eye)  \ Narrow caucfSI 13-17 anal rays  Chum  salmon  attractive  fish.  are In  water  they  blue  and silver,  salt  are metallic with  occasional black speckling on the in back.  They are  the latest of the five salmon species to enter the southern streams and rivers to spawn, usually in late autumn and in some instances in late winter.  In northern  rivers, however, they arrive on the spawning beds as early as July. Chums are widely dispersed along the Pacific coast from northern California up to the Aleutian Islands, in the Bering Sea and the Mackenzie River, inhabiting more than 875 rivers and streams in British Columbia alone.  While some have been known to weigh 35 lbs, chum salmon average 7.5 to 10 lbs, and can measure more that 100 cm at maturity.  They are harvested primarily on  their return to their spawning streams. Because their flesh is pale and low in fat content, chum salmon are not considered prime fish for canning. As a result, they are usually marketed fresh, frozen or smoked, although some canning does take place.  Figure 5 - Sockeye Salmon Best  know  salmon,  of the Pacific  sockeye  are  the  most sought after for their superior  flesh,  color and  quality. Their rich oil content Body slender and firm  13-18 anal rays  and red colour are  factors  that make them a favorite with the Canadian and international consumers. Although all Pacific salmon feed on shrimp and other crustaceans, these are the main diet of the sockeye, which many believe induces the rich colour and oil content.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  7  Sockeye were the first salmon to be canned in quantity and are still the mainstay of the canning industry, which started in British Columbia in 1870. By 1876 three canneries were established in the province and expansion was so rapid that by the turn of the century, 65 canneries were in operation. The number peaked in 1917 with 84 canneries, but gradually declined as canning technology improved and salmon became scarcer, especially sockeye.  Their size varies with age: a four-year-old sockeye averages 7 lbs, while older fish will run to 12 lbs. After a number of years at s e a , sockeye return to their home streams to spawn. In the majority of southern BC rivers and streams, sockeye return as four-year-olds, but in northern rivers of the province, five-year-olds are about as common, and still farther north in Alaska, six-year-olds are in the majority. Some eight-year-old sockeye are also found in northern rivers.  Figure 6 - Pink Salmon  Pink S a l m o n  Smallest but most abundant Larqe oval spots  Scales small  of the  west coast salmon,  pink salmon are known fishermen Soft body (limp fish)  or 17 anal rays  as  "humpies"  extremely  to  "humpbacks" due  to  the  humped  back  developed by the males as they return to spawn. The females do not exhibit this same change during spawning. Because of their fixed two-year life span, even-andodd year pink stocks are effectively isolated from each other.  When the young 2.5 cm fry emerge from the gravel beds the following spring, they go directly downstream to the ocean. During their first summer in salt water, they stick  close to  shore,  moving  offshore  in September.  Rich ocean feeding  in  subsequent months induces remarkably rapid growth, bringing their average weight to 5 lbs at maturity with some reaching a weight of 10 lbs and a length of 76 c m .  This species is found in streams and rivers from California north to the Mackenzie River, with their principal spawning areas between Puget Sound, Washington, and Bristol Bay, Alaska. When pinks enter the ocean, they feed at first on plankton, but gradually turn to more active prey.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  8  In spite of their short life span and small size, their migrations are extensive, covering thousands of kilometers from their home streams.  Millions are caught  along the coast of BC and Alaska as they return to spawn. This species is fished by gillnets, purse seines and by trolling gear. They are also caught by sport fishermen using artificial lures. Most pinks are canned; some are sold as fresh fish. Many more, especially of the troll catch have been frozen in recent years.  1.1.3 JS McMillan Fisheries Ltd. A privately owned family business established in 1953, JS McMillan Fisheries Ltd. (JSM) is a Vancouver-based company with operations in nearly all levels of the commercial fishing industry, from supply through distribution. JSM is one of the top four companies in terms of capacity of processing fresh and frozen products. JSM employs over 250 people in two plants and this number is higher in peak the season. JSM outsources its canning operations to Great Northern Packing Ltd. Facilities of this canning company are within JSM plant in Prince Rupert and Great Northern Packing Ltd. employs around 100 people.  JSM has two plants: one in Vancouver and one in Prince Rupert. The plant in Prince Rupert is bigger than the plant in Vancouver and it processes American (i.e. Alaskan) and BC salmon and ground fish. During the project I had an opportunity to visit both plants. I spent one week observing the peak salmon season in Prince Rupert; this experience was very informative and relevant to my study. I achieved a greater understanding of the company processes and operations, and learned many new things about the company, its structure and people. It is really informative to listen to employees and managers about the company and its operations and see by yourself. Observing the production, modifying and verifying the optimization model (separation of hand and Ryco lines in the model, better understanding  of fish  deterioration, by-products, multi period supply, addition of a new product category; selling fresh), giving project information to managers and production people in Prince Rupert, delivering the CAT were the main points of the visit.  While collecting information,  I spoke with several JSM employees. Some of the  information was subject to each person's interpretation.  For example, when I asked  different people about a typical catch size, they tented to remember the biggest  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  9  catch, not the average catch. Though, most of the time the answers were similar, there  were  times  when  further  questions  were  necessary to  gain  accurate  information.  The main product categories for JSM are fillets, fresh, frozen, canned, fishmeal (called animal meal as well) and the main by-product is roe. The main production lines are hand, Ryco, filleting, and canning, freezer tunnel and reduction plant. The landed fish are unloaded from fishing boats using pumps, put in a tank for temporary storage and then graded into different totes according to size, quality, skin colour. These totes are then sent to the different production lines, shipped to Vancouver or another buyer, or put aside or into the freezer to be processed later. Keeping the fish in the inventory and processing later is a common practice. Please see Appendix 1 for a summary of the production process.  Fish are classified according to size, quality and skin colour.  This task is based on  grading people's experience and it is a subjective process. For fish categories and product list for chums please see Appendix 2 .  More grading or selection can be done in the cannery department as well. If the fish is in good quality and fresh, it is separated to another tote and processed in a different line or sold as it is. It is sometimes possible to freeze the fish and process than later but management does not want to do this. Although, this can be done if there is not enough capacity available, they would like to process or sell the salmon fresh and store after processing so that fish does not deteriorate much and lose its value.  Salmon can be processed in any production line according to requested end product. Hand lines and Ryco lines are very similar; the only difference being that Ryco has a machine that cuts fish heads off automatically. Ryco is a production line where salmon is dressed using a machine that cuts the head off. Refer to Appendix 3 for Ryco and hand lines. The Ryco is faster and cheaper to operate but when JSM needs fish with heads, hand lines must be used. Moreover, if the fish is heavier than 12 lbs it must be processed in the hand line since the Ryco line cannot work with fish heavier than 12 lbs.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  10  Head-on products slow down the production but make more profit per unit. The customers for these products are most commonly Japanese restaurateurs and these head-on products are prepared for visual appearance. The reason that the hand-on fish slow down production is that they require more labour and the Ryco line cannot be used.  Fresh products are prepared in hand and Ryco lines and then sent to the packing department. Similarly, frozen products are prepared first in these lines, then sent to the freezing tunnel and then to packing. Fillets are prepared in filleting lines and if required are sent to the freezing tunnel as well.  In the canning department canned products are prepared. The canning department sometimes receives dressed fish but usually " r o u n d " fish. The canning department 1  2  needs to remove bone, fin and tail as well as head and viscera. Therefore all fish, even dressed, go through the same process in the cannery department.  This means  that JSM incurs dressing cost twice. The reason this occurs is that sometimes the fish quality cannot be determined unless it is dressed. First it is decided that fish quality is good enough for fresh or frozen products but after some time it is observed that fish is no good for fresh or frozen products and it is send to cannery. Cannery fish must not be too fresh or too old for a smooth production otherwise the machine that cuts the fish into small pieces does not work very well and it becomes difficult to process. Neither fresh nor old fish are the best for the cannery. Unfortunately, at the moment it is not always possible to plan this fish processing in the cannery but the hopefully CAT will make a better job.  Animal meal and fish oil are produced in the reduction plant. Other companies send their leftovers to JSM's reduction plant to generate those products. Ten years ago the reduction business was very profitable because the animal meal price was quite high, however, at the moment the price for this product is not very profitable.  Roe is taken out in the Ryco, hand and cannery lines and then processed in the roeprocessing unit. It is separated into eggs and packed into small boxes ready for shipping.  Fish viscera, head and tail removed (some fish may have the tail on or head on) but with skin and bone retained. Unprocessed fish  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  11  T h e b o x e s f o r p a c k i n g are p r e p a r e d prior to p r o d u c t i o n . Fish is k e p t in t o t e s w i t h ice in f r e e z e r s a n d t h e n p a c k e d , iced a n d s h i p p e d . Or it c a n be k e p t in t h e f r e e z e r for f u t u r e s h i p p i n g . M a t e r i a l h a n d l i n g is d o n e u s i n g t o t e s a n d f o r k l i f t s . T h e fillets t a k e 15 m i n u t e s in t h e f r e e z e r t u n n e l to f r e e z e , w h e r e a s d r e s s e d s a l m o n t a k e s 4 5 m i n u t e s . J S M has its o w n i c e - p r o d u c i n g unit a n d it p r o d u c e s its o w n ice f o r o p e r a t i o n s . F r e e z e r c a p a c i t y f o r t h e f i n i s h e d p r o d u c t s is b i g g e r t h a t t h e o n e for s e m i - f i n i s h e d p r o d u c t s . J S M u s e s t h e s e t w o d i f f e r e n t f r e e z e r s at t h e s a m e t i m e s i n c e t h e i r l o c a t i o n s a n d d i s t a n c e s f r o m t h e p r o d u c t i o n a r e different. T h e f o r m e r o n e is o u t of t h e w h e r e a s the latter one  is in t h e  production  plant  plant. T h e r e is a l s o o n e f r e e z e r  in  V a n c o u v e r . R e f e r to Appendix 3 f o r Prince R u p e r t plant's h i g h l i g h t s .  T h e q u a l i t y d e p a r t m e n t s a m p l e s a n d c h e c k s i n c o m i n g fish a n d f i n i s h e d p r o d u c t s . It p e r f o r m s r a n d o m s a m p l i n g a n d r e c o r d s t h e d a i l y l a n d i n g s by s p e c i e s per boat. T h e y r e c o r d fish q u a l i t y a n d fish s i z e d i s t r i b u t i o n , t h o u g h no r e c o r d s a r e k e p t of t h e c a t c h a m o u n t . T h e c a t c h a m o u n t d a t a is k e p t by a c c o u n t i n g . H o w e v e r m o s t of t h e d a t a a r e in d i f f e r e n t t r a n s a c t i o n p a p e r s o r in o t h e r r e c o r d s a n d is difficult to o b t a i n .  The  quality department  contacts  with  fishing  k e e p s t r a c k of fish d i s t r i b u t i o n s i z e a n d  boats  help  to  determine  c h a r a c t e r i s t i c s of  quality.  Radio  incoming  fish.  C o m b i n e d , t h e s e efforts help to f o r e c a s t p r o d u c t i o n e s t i m a t e s . It is i m p o r t a n t to n o t e t h a t t h i s s t u d y is n e w a n d t h e r e is not y e t m u c h d a t a a v a i l a b l e to d e t e r m i n e t h e f i s h size distribution and other statistical information.  E a c h e m p l o y e e c a n w o r k in d i f f e r e n t w o r k s t a t i o n s . For e x a m p l e a forklift d r i v e r c a n h e l p b o x i n g a n d g r a d i n g t h e fish a n d t r a n s p o r t i n g t h e t o t e s . H o w e v e r , t h e r e a r e s o m e t a s k s , s u c h a s g r a d i n g or forklift d r i v i n g , w h i c h r e q u i r e s p e c i a l k n o w l e d g e a n d e x p e r i e n c e . M o r e o v e r , J S M c a n call s e a s o n a l w o r k e r s if it is n e e d e d , a s o v e r t i m e w h i c h is a c o m m o n p r a c t i c e .  Most  of  the  time,  JSM  can  learn  about  the  catch  (amount  and  size,  quality  c h a r a c t e r i s t i c s ) t h a t is c o m i n g to t h e p l a n t 10 to 2 4 h o u r s in a d v a n c e . H o w e v e r , if t h e b o a t is a g i l l n e t or s m a l l C a n a d i a n boat, t h e i r i n f o r m a t i o n m a y be m i s l e a d i n g b e c a u s e t h e s e b o a t s a v o i d m a k i n g radio c o n t a c t s .  Even when they make  radio  c o n t a c t s , t h e y d o not w a n t to g i v e t h e a c c u r a t e i n f o r m a t i o n on t h e r a d i o b e c a u s e of t h e p o s s i b i l i t y t h a t o t h e r b o a t s c a n h e a r t h e m a n d m a y s t a r t f i s h i n g in t h e s a m e  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  12  area. I asked if an encryption system could be used and JSM staff told me that they had already tried this in the past but it did not work. Therefore, JSM learns about the catch size and composition only after the boats arrive to the plant. There are usually 30 to 40 gillnetters that bring fish to JSM at different times in the season. Alaskan boats' information always reaches JSM before they come to the plant as they stop in other ports on their way. Their estimates are accurate and they provide good information since the boats that bring fish to shore are usually boats that collect fish from smaller fishing boats. Alaskan boats bring the majority of the fish. This is because Alaska has more catch openings and more fishing areas.  Fishing boats are independent. When the fishing is opened, they go to that area and start fishing and return to fish processing plants. They may go to any plant but they go to plants where they have contacts, trust and previous working experience with. Payment to these fishermen can be done right away, or at various time periods throughout the season. There are different type of boats, some that do the actual fishing and others that collect the fish from those fishing boats and brings the catch to the JSM plant. Fishing boats differ considerably in size such as trollers  or  gillnetters.  If the Prince Rupert plant gets more fish than its capacity they have several options; they can sell fresh fish to other fish companies, send fish to the Vancouver plant, or store and process later.  After JSM learns or forecasts how much fish is available, marketing people call their customers and try to estimate demand. Sometimes they promise certain products to their customers based on forecasts or market conditions and do whatever they can to fulfill those orders. Other times, in order to fulfill these JSM ends up buying fish from other fish companies and giving priority filling these orders. When JSM buys fish from other fish companies to fulfill the promised order to their customers, the company's profit margin decreases considerably.  One might question why JSM does not make their order arrangements after a good allocation  and  production  plan are  prepared. At the  moment  the  marketing  department controls the order arrangements. With the tool proposed in this project, JSM can first decide the most profitable catch allocation plan and then decide what  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  13  and how much to sell. Or they can see the effect of different catch allocation plan outcomes by solving different market demand scenarios.  Everyday the management in Prince Rupert communicates with the Vancouver plant and decides the production schedule. Currently, they make decisions based on experience, intuition and past outcomes. Things may change during the day; either a new catch may arrive or JSM may learn about a new boat arrival or catch opening. The problem that needs to be solved is to generate an effective catch allocation plan while maximizing profit according to expected input parameters. JSM has just started to record the daily production information and this will be useful for us to compare the tool's suggestions and their actual production figures.  JSM is considering an implementation plan as follows: the tool will be installed on different PCs in the production department, as well as on the main server. This will allow various analyses by production individuals, with the final schedule saved on the server. The company has enough computer power and networking ability to achieve this. Another suggestion for implementation is to have one person responsible for entering the necessary inputs to the program and solving the schedule prior to the teleconference call with the Vancouver plant.  This would allow discussions to be  focused on actual scenarios of each planning horizon.  1.2 SUMMARY OF PREVIOUS WORK The project was started on July 2000 by a former COE student, Laura Morrison. She was the first project analyst and she did the first analyses, developed the first model and built the first tool. Then I became involved into the project in September 2000. My work during this time (Sep 2000 - Dec 2000) was getting familiarized with the project,  to help in the development of the initial  model and develop a small  prototype of the model in AM PL by using a database to access the data. The reason 3  we developed the model in AMPL was because it was thought that AMPL might be used in the next phases of the project. This AMPL model represented the same version of the real model but excluded the recovery rates . Recovery rates were 4  AMPL: A Modeling Language for Mathematical Programming 4 It is a number between zero and one showing the percentage of the fish can be used to produce a particular product. JSM determines recovery rates by comparing the round fish and finishes product and by experience.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  14  excluded for simplicity on the first AMPL model. The AMPL model can be found in Appendix 4.  The work done by Morrison is reported in Morrison (2000) and can be summarized as follows: •  Initial data gathering about the fishing industry and JSM operations.  •  Building a prototype model and tool to show JSM the concept of the proposed model and tool.  •  Building the first LP model and first tool.  •  Presenting these to J S M .  The details of the M o r r i s o n ' s LP model and her initial tool can be found in her thesis. Morrison graduated in December 2000. I took the project over at that point and then I started to work on the tool and model. The first thing was developing the revised model (SPLP - Single Period LP) and the first software (CAT- Catch Allocation Tool). Refer to the chapter two for SPLP and CAT. Some of the tasks done were:  •  Adjusting the model to one shift per day over three days and allow increase in the capacity if required.  •  Addition of by-product  •  New user interface, addition of aggregate production reports, product reports,  5  modeling  inputs menus, help file (user manual). •  Addition of the input save function (so that users can save the catch inputs with time and date for future reference).  1.3 LITERATURE REVIEW Fishing industry harvesting; operations,  research is usually divided into three areas:  fish stocks  and  mostly dealing with fish population models, harvesting policies and fleet fish processing;  producing  production planning, fish marketing;  end  products  from  raw  materials  i.e.  dealing with different markets, customers and  products. There is a large amount of literature available in the area of fish population models and harvesting. Similarly some literature is available for scheduling fishing Some examples are roe, heads, tails and other leftovers from the production.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  15  boats and production planning. For example Forsberg (1996) gave a multi period linear programming approach to the production planning in fish farms. This includes the determination of optimal number of fry to transfer into the grow-out system, the estimation of population growth and production costs and the determination of the optimal harvesting. For fishing boats scheduling, Gunn, Millar and Newbolt (1991) studied tactical planning for a fishing company with integrated fishing and fish processing. The characteristics of the model included a fleet of trawlers, a number of processing plants, and market requirements. A linear programming model was formulated to determine the production plan and trawler scheduling to maximize profits. This model is in more aggregate level than our model and unlike our shortterm production model it is aimed at long-term planning. Moreover, it includes trawler scheduling but excludes other points such as fish deterioration.  In our project we need an optimization model that handles the production-planning problem  of  a  fish  processing  deterioration, and by-product  plant  with  some  complications  such  as  fish  modeling. Production-planning models in different  industries can be found in literature but there are not many papers that directly address fish processing. However general type of production planning models can be applied to fish processing with some modifications. An example for this type of study is given by Randhawa and Bjarnason (1995). The writers state that production planning in the fishing industry depends on raw material supply and the uncertainty and randomness in the raw material supply requires frequent decision on production plans for product mix, labor requirements, and raw material inventory carried from one period to next. They describe a decision aid for coordinating fishing (fishing boats) and fish processing at shore plants. Their suggested system combines a simulation model with a linear programming model. The simulation model analyses fishing boats operations, including catch generation, length of fishing trips, and trawler landings and the LP model uses the output from the simulation model to determine labor requirements, inventory levels, and production levels to maximize net revenues.  They use the simulation to generate inputs i.e. catch sizes, and fish quality levels for the optimization model by simulating trawler operations, length of fishing trips and trawler landings. The LP model uses the output from the simulation model to determine production levels, labor requirements and inventory levels to maximize  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  16  net revenues. Moreover, using the output of the optimization and simulation trawler schedule is generated.  There are some significant differences between Randhawa & Bjarnason's approach and our approach. JSM has no control on fishing boats operations since boats are independent. In our model we are using the company's best guesses for the catch size and catch composition because of the seasonal salmon runs and department of fisheries regulations as discussed in the previous sections. Moreover, they take into account the labor planning into the production planning. However, JSM has a very flexible work force and most of the workers are seasonal workers so we did not include  labor  planning  into  our  model.  However we  needed to  include  fish  deterioration and by-products modeling. Our fish deterioration modeling approach is different than the one presented by Randhawa and Bjarnason. In our approach fish either stays in the same quality level or goes to lower quality level, whereas in their model the fish should be processed in a certain amount of time and the quality remains the same before processing.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  17  2 INITIAL APPROACH 2. l SPLP - SINGLE PERIOD LINEAR  PROGRAM  The initial approach for the problem was to develop a linear program and a decision support tool for chum catch allocation. The model was designed to generate a 3 day production plan. Chums are the one of the three salmon species that JSM buys and processes for the end products. JSM wanted to start to the project with only chums because of the following reasons:  o  JSM wanted to give priority to chums in processing since they must be processed as soon as possible after landing to get the most value out of roe.  o  JSM wanted to develop a simple model at the beginning and then extend this to other species and complications.  Linear Programming (LP) is the modeling and optimization approach for the CAT. The objective is to maximize the total profit over the planning horizon given to the constraints of the production processes and products. With the LP approach we can easily find the optimal catch allocation for a large number of different fish types, a large number of end products and a complicated constraint structure in seconds.  Since it was the first attempt to model JSM's production processes some of the characteristics of real processes were relaxed or were not included in the model to simplify the modeling and understanding of the model. These points were fish deterioration, multiple species, by-products and multi-day supply. However byproduct modeling is very important. Since it could be added to the model very easily, this feature was added to this initial model. The assumptions of the model are:  6  o  Fish quality is the same over 3 days i.e. there is no deterioration of the fish,  o  Product mix is known.  o  The supply only arrives at the beginning of day 1.  o  Supply  6  o  Amount of the total catch is known.  o  Proportion or amount of each type of fish is known.  Fish types, product list and feasibility matrix showing which product can be produced from which types of fish.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  18  o  By-products o  Net profit coefficient for each main product, is calculated as weighted average of profit coefficient and recovery rates of the main product and by-products.  o  Profit coefficient for each product and recovery rate for each product 7  and by-products are known.  SPLP in words: Maximize {total profit for the 3 days} Subject to: Capacity constraints Upper bound constraints Lower bound constraints (demand) Supply Storage constraints Non - negativity constraints  Supply day 1  Production day 1  Production day 2  Production day 3  Figure 7 - SPLP Graphical Representation As seen from Figure 7, the fish arrives at the beginning of day one, and then the model allocates this supply to the first, second and third day according to capacity availability. As we see there is there is no direct inventory, no fish deterioration and no future inputs. There are 48 types of fish and 52 products for chums. The model produces a feasible production schedule over 3 days. There are 7488 decision  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  19  variables and 375 plus non-negativity constraints. Refer to Appendix2 for fish type and products details.  2.1.1 SPLP Model The notation for the SPLP model is as follows:  X  = amount fish allocated to product j from type i fish for day k in lbs  jjk  Nj  = net profit from main product j in $/lbs  PM j = profit for the main product j in $/lbs Pb  mJ  = profit for the by - product m of main product j $/lbs  rj  = recovery rate for the main product j  b  = recovery rate for by product m of main product j  mj  C - capacity of canning on day k in lbs k  R = capacity of reduction on day k in lbs k  H = capacity of hand & Ryco on day k in lbs k  F = capacity of freezer on day k in lbs k  E = capacity of filleting on day k in lbs k  D  = demand for product j on day k in lbs  S  = supply of fish type i in lbs  jk  i  U T  j k  k  — upperbound for product j on day k in lbs = storage capacity on day k in lbs  C= set ofproducts processed in canning R = set of products processed in reduction H= set ofproducts processed in hand&Ryco F= set of products processed in freezer E= set of products processed in filleting  i= 1,2..,48 (48 different types of fish) j = 1, 2.. ,52 (52 different products) k=l,2,3 (3 days planning horizon)  The revenue - all costs for a particular product in $/lbs. JSM calculates profit coefficients by identifying the costs and determining the sale price.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  20  / / k  ZZ*,  zz*. / je R  ZZ*. ZZ*.  zz*,  k  *  ±c  Canning  k  ^  Vk  Reduction  Vk  Hand & Ryco  \/k  Freezer  \fk  Filleting  ' j^E  Zo *>  Demand  zz*,  Supply  j k T J tjk i r  x  ZZo  Nj  v/,v*  Upperbound  VJt  Storage  \/i,j,k  Non - Negativity  is the net profit coefficient for product j and it is calculated as the sum of profit  coefficient weighted by the recovery rates for the main product and by-products. This is to model by-products. By-products modeling is crucial for the optimization model. The reason is if by-products are not considered than some of the products will have negative profits and they will never appear in the solution. In reality, those products also have some by-products such as roe, which bring considerable revenue, enough to yield a net profit. Hence by-product modeling is essential for our problem.  The objective function is the summation of the net profit coefficient of a particular product multiplied by amount of fish allocated for that product from a particular fish type on a particular day over all fish types, for all products and for all d a y s ; i.e. total profit over the planning horizon.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  21  The first five constraints, called as capacity constraints, are capacity constraints for different production lines where Q , Rk, Hk, Fk, Ek represents available capacity, on day k for canning, reduction, hand\Ryco, freezer and filleting respectively. These capacity values will be determined and entered to the model by production people at JSM depending on labor and other resources availability. These constraints ensure that the amount of salmon allocated and processed in a particular production line does not exceed its capacity. Each production line has a certain capacity but the it can be adjusted by increasing or decreasing the number of people working in that line, altering machine speeds or making an extra shift. The model can be solved for different capacity values and a capacity change decision can be given by judging the improvement or deterioration in the objective function.  The next constraints are estimated demand constraints and these ensure the amount of processed fish for a particular product on a particular day will be met. In other words, these constraints provide a lower bound for the amount of production for each product. If there are no market commitments these numbers should be set to zero so that they will not have any effect on the LP.  Supply  constraints are conservation type  of constraints  and  make sure  that  processed fish amount is less than or equal to what the plant has available for production. In this model, for each type of fish there is only one supply value to be entered to the tool and it is the first day's supply value. This value needs to be estimated by JSM even the first day's supply is exactly known since supply figures for the second and third day should be taken into account for a good production plan. JSM estimates these by evaluating the fishing openings, number of boats, forecasts and the information JSM has in that particular week.  Very similar to demand (lower bound constraints) we have upper bound constraints, which put an upper bound on each product. If we do not have these in the model, the LP will allocate all the available resources to the most profitable product but in the market we will not be able to sell that much. The upper bounds will be set using the company's experience. There are some certain types of products such as fresh types that should be consumed sooner than others such as frozen ones and hence the upper bound for a fresh product should be determined more conservatively than the other one. The marketing department knows that how much of a particular  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  22  product can be sold in the market by past experience and they can update these according to market conditions. If there are no upper bounds these values should be set to very high so that they will not have any effect on the LP.  The next type of constraint is for storage capacity. This is very similar to capacity constraints making sure that we do not produce more fish than storage capacity. One question might be does the capacity of the storage depend on the previous products inside the storage? The answer is no since if the previous products in the storage will not be shipped immediately they are transferred to bigger storage area outside of the production facility and that one is significantly large to accommodate the end products. Otherwise the finished products are transported immediately to the trucks for the shipment. Another point is that products of canning and reduction lines do not require this storage space and they are not included in this constraint. Frozen products are also excluded since they have to be processed in hand & Ryco before the freezer and they are already considered together with other products (fresh) of hand & Ryco line.  The final sets of constraints are non-negativity and they force all the decision variables to be non-negative. If these constraints are omitted then the model may allocate negative values to decision variables since some of the profit coefficients are negative.  The LP model was tested during and after the development of the decision support tool by verifying assumptions with JSM management and making some test runs; one example can be found in chapter four. The CAT and LP model were presented to JSM during company visits.  This model as discussed above was developed for chums only and it has 52 different products and 48 types of fish. It provides a solution for the production schedule over the planning horizon. The planning horizon is 3 days and this model will be solved each day with the new data. This is called rolling horizon approach.  SPLP does not include fish deterioration modeling, multi period supply and multiple species. It was the first attempt to model the process. It familiarized JSM with the LP approach  and  helped us to  understand  JSM processes and operations.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  After  23  developing this model, JSM had more information about linear programming and their operations. And we gained expertise about company operations. Both JSM and COE explored improvement areas and JSM wanted us to build more sophisticated models to capture all the relevant aspects of the problem.  2.1.2 Catch allocation tool - CAT JSM required a reliable and fast tool for decision-making in the salmon season due to the complications of the salmon business. Some of the complications are control of the department of fisheries on catch openings, the uncertainty involved in catches and demand for the end products. JSM would also like to increase its  profitability,  automate the decision making process, decrease the decision making time and expert dependency.  The catch allocation problem can be solved optimally with a linear program. However it must be easy and simple enough for the end users. They usually need to solve this problem again with different input parameters, and get the production schedule in understandable format everyday. There is also need for user error checking, and saving the inputs for future reference. For these reasons we need a decision support tool that will provide a friendly interface and provide these functions for the end users so that they will be able to use linear programming easily for daily decisionmaking. Therefore, LP alone is not sufficient for our goals in this project. There are two main aspects to this project, the linear program formulation and user interface development.  Optimization  solves the  catch  allocation  problem  optimally  and  software development is used to build the decision support tool.  CAT has been built. It allows the users to enter the decision-making inputs and get the production schedule as output. The LP calculations are done at the backend. CAT also saves the inputs for future analysis and reference. User interface is designed according to JSM preferences and a help file (user manual) was prepared and included into the software. It was developed in MS Excel with VBA (Visual Basic for Applications), and it uses an external LP solver. The solver is a spreadsheet solver named Large Scale LP solver by Frontline Systems. User interface of the LP solver is very similar to standard solver in Excel. However it is more powerful in terms of time  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  24  and number of decision variables. It is hoped that CAT will save time and increase profitability for JSM by finding an optimal production schedule in a very short time.  The LP model and software tool have been built and tested and they are ready for the implementation. CAT was delivered to JSM in Prince Rupert visit. JSM started to collect information about their actual production and they will first try CAT and then start using it in their daily decision-making. A detailed description of the CAT software with user interface screen shots can be found in  Appendix  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  5.  25  3. REVISED CATCH ALLOCATION PROBLEM During the project, both JSM and COE recognized there was an opportunity  to  improve the previous model and tool by relaxing the single day supply and single species assumptions of the first model by including a model for deterioration and considering the other points that we did not include in the first model.  The nature of the problem is very similar to multi-period  production  planning  problems. The excess fish, that is, fish which cannot be processed today remain in the inventory and become available for tomorrow's production. The challenge is that fish is a perishable good. Fish today are not of the same quality as fish tomorrow. This deterioration can be effected by many factors such as the conditions of the fish when it is landed and the storage space. Fish deterioration was a big concern to the company in our June meeting and the management mentioned that this is very crucial for the tool to capture the practice.  One of the assumptions for the initial model was the supply arrives only at the beginning of day 1 but this is not very realistic. In the salmon season the company can get fish every day and sometimes more than once during a day. Moreover this type of modeling for a production planning is more common practice.  JSM can receive different species (chum, pink, sockeye) at the same time. JSM gets fish from both Canadian and American fishing boats coming from different regions of the Pacific Ocean. All these boats can arrive with different types of fish and these species will compete against each other for the capacity and other resources available.  3.1 MPMSLPMULTIPERIOD MULTI SPECIES LINEAR PROGRAM The revised problem definition  is the same as the previous problem  definition  namely, finding the most profitable production schedule, but it more realistically models the actual process. The additions are fish deterioration, multi period supply and multiple species modeling.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  26  A s s u m p t i o n s of t h e m o d e l :  Although we relaxed or removed some of the assumptions of the initial model in this new model there are still some assumptions that we have to make in order to model the  process. Before starting  to  build the  model, these  (new  and old ones)  assumptions were verified several times by talking with JSM staff. Moreover with company visits I had opportunities to go over these assumptions once more by observing the real system processes and talking with people involved in those. There is one new assumption for deterioration. Each type stays or deteriorates to a lower type with a certain probability each day and those probabilities are known.  The other  assumptions: product  mix,  by-products, supply assumptions  (catch  amount and fish type amounts) are the same with SPLP.  M P M S L P M o d e l in W o r d s :  Maximize {total profit for the 3 days} Subject to: Capacity constraints Upper bound constraints Lower bound constraints (demand)  1  Supply and Inventory constraints Fish deterioration constraints Storage constraints Book keeping (artificial) constraints Non - negativity constraints  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  27  Supply day 1  Supply day 2  Supply day 3  Inventory day 0 Inventory day 1  Production day I  Inventory day 2  Production day 2  Inventory day 3  Production day 3  Figure 8 - MPMSLP Graphical Representation In Figure 8 the inventory day 0, supply day 1, supply day 2 and supply day 3 arrows represent the necessary inputs to the model. The model finds the optimal solution and gives the production and other inventory arrows as output.  Supply d  Supply day d+1  1 MPMSLP  Deteriorated Inventory day d-1  Production day d  Deteriorated Inventory day d  1  New Inventory day d+1  Production day d+1  Figure 9 - Deterioration Representation Figure 9 represents the deterioration process. At the end of day d, unprocessed fish deteriorates and become available as deteriorated inventory for the production on day d+1. The model is solved using a rolling horizon approach; each day this model is solved for the next planning horizon, taking today as the first day in the model with the new estimates of future supply.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  28  3.1.2 MPMSLP Model F  stpd  = quantity of type t fish of species s used for product p on day d in lbs  P  sp  = net profit coefficient for product p of species s in $/lbs  PM  = profit coefficient for main product p of species s in $/lbs  PB  = profit coefficient for by - product m for product p of species s in $/lbs  ps  mps  RM  = recovery rate for main product p of species s  RB  = recovery rate for by - product m of product p of species s  sp  spm  C = capacity of canning on day k in lbs k  R = capacity of reduction on day k in lbs k  H = capacity of hand & Ryco on day k in lbs k  F = capacity of freezer on day k in lbs k  E = capacity of filleting on day k in lbs k  S  = storage capacity on day d in lbs  d  U  = upper bound limit for product p of species s on dayd in lbs  D  spd  = lower bound limit for product p of species s on dayd in lbs  S  sld  = supply of type t fish of specie s on day d in lbs  I  = deteriorated inventory of type t fish of species s on day d in lbs  spd  s!d  B  sld  M  jt  = new inventory of type t fish of species s on day d in lbs = entry of row j, column t of deterioration matrix  C= set ofproducts processed in canning R= set ofproducts processed in reduction H= set ofproducts processed in hand & Ryco F= set ofproducts processed in freezer E= set ofproducts processed in filleting  s= 1,2,3 (3 different species) t = 1, 2.. ,T (T different type offish - T is the maximum number for fish types of 3 species) p - 1, 2.. ,P (P different products - P is the maximum number products of 3 species) d=l,2,3 (3 days planning horizon) m=l,2..,M (M different by products M is the max number by- products of 3 species) j is a dummy index for fish type  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  P  = PM  sp  RM  sp  +Y PB m  sp  J  spm  RB  spm  Vs,p  (1)  Equation (1) shows the net profit calculation. It is a weighted average of the recovery rates and prices of the main product and by-products. By doing so we take into account the by-product profitability.  w  «*XX^XX  s p subject to  swd  F  t d  S S !S Fstpd — Cd s t peC  V d Canning  XXX-^ -K  yd Reduction  s  peR  1  XXX^V* - d  W Hand & Ryco  H  s t peH X X X F s t peF  stpd  V d Freezer  ^ F  d  XXX ^stpd — Ed s t peE  V d Filleting  XX  V d Storage  zZ stpd ~ d F  S  1 pfzH.E  s  X W *'pd — U d t  \fs,p,d  Upper bound limit  X ^sp stpd — Dspd t  V s,p,d  Lower bound (demand)  S td  Vs,t,d  Supply & Inventory  \/s,t,d  Deterioration  Vs,t,d  Artifical  R  F  sp  F  s  +  I st,d-l  =  std ~ ^X stpd p  F  F  F d * 0 slp  B d std +F  sl  Vs, t, d, p Non - negativity  The objective function is total profit over the planning horizon. It is the summation of the fish allocated to a particular product times the net profit coefficient of that product for all fish species, fish types, products and days.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  30  The  capacity  (first five  constraints),  storage, lower  bound  and  upper  bound  constraints are very similar to the SPLP capacity, storage, lower bound and upper bound constraints respectively. One difference is that in this model, we have constraints for all species.  The next constraints are to balance inventory. Supply and inventory are bookkeeping constraints and they ensure that the daily fish supply plus inventory from the previous day equal to production  plus the unprocessed fish remaining in the  inventory. So supply and inventory constraints balance inputs and outputs. In these equalities S represents the supply, F represents the production (fish allocated to production), I represents the unprocessed fish from the previous day and B is the inventory at the end of the current day.  Fish deterioration constraints convert the previous day's unprocessed fish to today's fish by allocating them to lower quality levels according to fish quality deterioration matrix.  In order to model the deterioration the following is assumed; fish have a certain quality level (state of the fish and in the model it is called type, this idea was also used in SPLP to classify fish inputs to types) and in the next day if it is not processed it can stay in that type, or it can deteriorate to a lower quality level (i.e. lower type) with known transition rates.  For example, assume there are 4 types in the model with type 1 corresponding to the best quality and type 4 representing the worst quality (sent to the reduction). Suppose the transition rate matrix is given in Table 2.  Type 1 2 3 4 1 0.2 0.5 0.25 0.05 2 0 0.6 0.3 0.1 3 0 0 0.35 0.65 4 0 0 0 1 Table 2 - Fish Quality Deterioration Matrix  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  31  Let I  id  - deteriorated inventory level for type i fish on day d  B(d= new inventory level for type i fish on day d  Observe that the sum of rates for each row equals 1. In this example we see that type 2 fish stays in this quality level with the rate 0.6. So if we have 10 lbs of type 2 fish only 6 lbs will be available for the production for type 2, 3 lbs will be type 3 and the remaining 1 lb will be type 4 and type 4 fish will remain always in that state until reduction. Then the deterioration equations for this example will be:  / / = 0 . 2 * B, d  (2)  d  I =0.5* B +0.6* B 2d  ld  (3)  2d  I =0.25* B +0.3* B +0.35* B 3d  ld  2d  /, =0.05* Bi +0.l* B +0.65* B RF  d  2d  (4)  3d  3d  +1* B  4d  (5)  If we have 100 lbs type 1, 50 lbs type 2 and 40 lbs type 3 and 20 lbs type 4 fish in day d and if we do not process them on day d then at the beginning of day d+1  •  20 lbs will again be type 1 fish from equation 2.  •  50 + 30 = 80 lbs will be type 2 fish from equation 3.  •  25 + 15 +14 = 54 lbs will be type 3 fish from equation 4.  •  5 + 5 +26+20 = 56 lbs will be type 4 fish from equation 5.  The last two set of constraints in the LP model are artificial and non-negativity constraints. Artificial constraints make the conversion of more specific decision variables (F ) stpd  to more aggregate variables (F ) since in some of the constraints sld  we have to use the former but in some others the latter. These decision variables should be linked and artificial constraints do this. On the other hand, non-negativity ensure all the decision-making variables be positive and prevent the model allocating negative values to decision variables when some of the profit coefficients are negative.  The size of the model will be certain after JSM specifies the exact fish types, product lists and other inputs for the LP model. There are 3 species and I believe that  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  32  sockeyes and pinks will have less number of products and fish types than chums. If we assume, the same number fish types and products for sockeyes and pinks as chums then there will be 3 times more decision variables {22464) than in SPLP plus inventory variables (864) in MPMSLP.  With the same assumption there will be 2247  constraints plus non-negativity constraints.  Since at the moment we do not have the actual inputs for the model, a prototype was developed for testing the modeling idea. This smaller prototype helped us to: •  Test and verify the MPMSLP model.  •  Verify modeling assumptions with JSM staff.  •  Develop a plan for CAT2.  The MPMSLP model has been developed. A prototype of the software tool has been built and tested. We are ready to build the new software when JSM decides the exact content and context of the new model. Please see  Appendix 6  for the user interface  of the prototype.  3.2 EXTENSIONS TO  MPMSLP  During the Prince Rupert visit I had the opportunity to observe the company's daily operations and its daily practices. Moreover I had many opportunities to discuss and ask questions about operations. Here are the some of the findings from the trip regarding this new MPMSLP model.  It seemed that some of the products and fish types that we have in the current chum tool are not used in Prince Rupert plant. Currently, JSM uses fewer fish type categories and products in classifying fish types and products. It had been agreed to keep the same rich fish type and product structure for other fish species in the new MPMSLP. The number of products and fish types might be reduced, if the similar products  and  fish  types  were  combined  and  aggregated  together.  And  the  management can interpret results easier. The reason why I reached this conclusion was that I observed that production people in Prince Rupert at the moment are not using as many fish types and products as we do in our current chum model. So I  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  33  thought it might be more useful if we decrease the number of input fish types and products. This idea was suggested to JSM management but they said that they would lose profit opportunity if this happens. The reason is that with the decision support tool and computational power that have been delivered to them, they can use richer product and fish type differentiation to optimize their planning and get more profit. One interesting point is that at the moment JSM is not using this many fish and product types. But the management would like to apply this type of product and fish type structure in the future and we are in that sense helping them to organize or improve their operations.  Some other points for the improvement of MPMSLP and MPMSLP tool (CAT 2) were: In the software for roe by-product, the ratio of male and females could be stated. This ratio is usually %50 - %50 but the ratio can be adjusted if needed. 4 different roe products will be added to by-products for future potential products. Moreover 4-6 "adjustable products" will be added into the LP model so that they can be used 8  when something extraordinary happens. JSM sometimes sells round fish to other fish plants. This is done for two reasons, either there is not enough capacity to process the incoming catch or selling to another plant is more profitable due to shortage of round fish in that plant. In order to include this, round fish will be added into product categories for each species and its recovery rate will be set to one.  The Ryco line and hand line constraints could be separated since they are physically different lines and their capacities and production speeds can differ according to type of the product they are producing. The hand line can produce all types of products that the Ryco line can produce with a slower speed but the hand line can also produce some of the products that cannot be produced by the Ryco line such as head-on products.  As a results of these observations, the LP model was revised since both head-on and head-off products can be produced in hand line whereas in the Ryco line only headoff products are processed. The changes to MPMSLP model are as follows. We introduce the additional variables.  These products do not belong to a specific category. Their input types can change from time to time and are produced seldom.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  34  H  = capacity for production hand line on day d in lbs  d  Y  = capacity for Ryco line on day d in lbs  (l  Free = free capacity for head off products in hand line after producing d  head on products on day d in lbs  The revised MPMSLP is almost the same as previous MPMSLP except the modification of capacity constraints for the hand line and the Ryco line.  Hand line capacity  constraints make sure that head-on fish are produced in the hand line only and the remaining capacity (free) can be used to produce head-off fish together with Ryco line capacity. All the remaining (objective function, indexes and their dimensions, constraints except capacity and non-negativity) are the same as the first MPMSLP model. Changes in the model are as follows: ZX ^ !F + Free = H s t pehead on slnd  d  Vd  d  Z X X F , d — Free +Y s t pehead off F , Free > 0 s P  d  d  d  d  Vd Vs, t, p, d  Handline capacity Ryco & handline capacity Now - negativity constraints  3.3 DATA REQUIREMENTS OF THE MODEL The MPMSLP model requires more data than the SPLP model. The additional data requirement is the deterioration rates. Product and fish types, product mix for pinks and sockeyes are also needed. Although the deterioration rates can be changed using the decision support tool, the product and fish type, product mix information was needed to start coding the software. In order to get this information an Excel data template was prepared and sent to J S M . Currently, JSM and COE are working together to prepare this information and to decide what should be included in the new model and tool.  3.4 A DECISION SUPPORT TOOL FOR  MPMSLP  Since the LP model was changed from the SPLP to MPMSLP there is a need for a tool that will use MPMSLP. Similar methodology will be used to build the new tool. Since  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  35  the new MPMSLP model is considerably larger than the SPLP model, a prototype was built for testing. The real one will be developed after getting the required information from JSM and reaching an agreement with JSM for the next steps of the project.  This new tool will be named CAT 2 (Catch Allocation Tool 2) and it will allow users to enter the decision-making inputs and obtain the production schedule as output. All the calculations and the optimization will work at the backend similar to CAT. CAT 2 will save all the inputs and outputs  in to a database for future analysis and  reference. The user interface will be designed for ease of use and to prevent potential user errors. In addition a user manual will be prepared and included in the software. It is planned to be developed in MS Excel with VBA (Visual Basics for Applications) and it will use an external LP solver. The logic and flow diagram of CAT 2 is in Figure 10:  Inputs  User enters the inputs to CAT 2  The inputs and outputs are stored in to a database  CAT 2 solves the allocation problem & generates the allocation plan  The allocation plan is presented as output  Figure 10 - The logic and flow diagram of CAT 2  Inputs are catch size and compositions, demand and upper bound limits, production capacities, deterioration probabilities, recovery rates and profit coefficients. Note that not all of the inputs will be changed from one day to another. Most likely only the catch information and demand figures will be changed everyday.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  36  3.5 PRA CTICAL ISSUES To test, verify and ensure the quality of the LP models and tools, the following steps have been carried out for CAT. Similar approach will be used for developing CAT 2.  Assumption  C h e c k i n g : The  assumptions  (supply,  deterioration,  by-products,  multiple species) used in LP models were validated and checked by testing and getting approval from JSM management.  Error  Debugging  &  T e s t s ; Like all software,  our tools  may  contain  some  programming errors. To minimize these errors, comprehensive checks and tests were and will be done for tools. More specifically, there were two main issues. One was ensuring that software is bug-free that is working as intended. The other one was ensuring the LP is working correctly. To prevent programming errors, coding checks and sample runs were  carried out  during  code generation  and  after  completion of the software. These tests were done in two ways. First one of them is code checking, a complete check of all the coding. The second one is running and solving some scenarios, checking, the outcomes and looking at the results. These tests also helped us to validate the LP models. Moreover for LP validation, more sample runs with different inputs were done with and without using the user interface these results were compared and some sensitivity analysis was done to ensure the optimality. It was seen that user interface and LP work fine and correctly.  C o d e D o c u m e n t a t i o n : Code documentation is writing explanations on the inside of the program code to help the programmers remember how the programming was done. This is especially very useful when any update or correction is required or a new programmer is involved to the project. This is done by writing explanations for each module, algorithm  or piece of code and by giving meaningful names to  modules, functions, objects and variables.  U s e r M a n u a l : Another name for the user manual is the help file. This is useful when a user needs some assistance for a particular function of the tool. This was designed for CAT in MS Word and HTML format and it is easy to use and refer to.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  37  S u p p o r t : The necessary support for users has been provided by company visits and other means of communication. And the software will be maintained and updated according to clients needs.  Some Measures: •  CAT runs in less than a minute and we are expecting CAT 2 will run in less than 5 minutes.  •  CAT crashes only if Excel crashes. This depends only operating system and Excel.  These measures are valid for both tools with a computer of at least 256 MB SDRAM and PHI 800 CPU. A minute is used as a benchmark here because in one of the meetings one of the contacts from JSM stated that running this tool in 15 minutes would be fantastic and even 15 minutes is much more below current decision process.  Some risks for the project are: Programming Errors: Preventive Action:  Comprehensive testing and checking were done to prevent these.  These checks were instant checking (just after code generation) by first going over the written code and then making some test runs. After each module was completed, once again a final check was done by inspecting all the code and making test runs. User Errors: Preventive  Action:  To make this errors minimum, warnings and checks were  designed in the software and user support was/will be given at the beginning of implementation stage.  Resistance  to  change:  Management, production  managers may show  some  resistance at the beginning due to lack of trust to the tool. This is expected to be minimal due to fact that this tool was developed in collaboration with JSM contacts who will use eventually this software. But still can be a problem. Preventive Action: Inform JSM about the usage and benefits of tool and show how to use  it.  Recommend a trial period that will  be helpful to compare the  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  tool's  38  performance with actual performance. Involve JSM staff at several key development steps.  Insufficient  data  and  information:  Deterioration  probabilities,  product  mix  information for pinks and sockeyes are necessary to build CAT 2 and implement MPMSLP. The strategy for getting the data is to tell the client the importance of these, work closely with them and get the required information.  Assumptions for the modeling: The models depend on assumptions made before development. These assumptions should be valid for the company  operations.  Deterioration, by-products assumptions are crucial for the model. Those assumptions were explained with all others to JSM management as early as possible and verified before and during the model constructions. Another help for the verification of those was company visits which gave us opportunity to observe the system closely and to see how the company really operates.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  39  4. RESULTS AND FINDINGS 4.1 IMPLEMENTATION Implementation involves presentation and installation of the decision support tool and giving user support. Our implementation recommendation to JSM is to start using the software for a trial period concurrently with the company's current system. This would help JSM to get familiarized with the tool and would enable us to compare the performance of the tool against the company's current system over some time period. It is also mentioned that the quality of the solution depends on inputs provided to the tool and the tool may need calibration in terms of its inputs.  Current decision making heavily depends on expert experience, knowledge and judgment. Most of the time the production decisions should be done very quickly. This could force decision-makers to make more ad-hoc decisions.  During my Prince Rupert visit, I observed production people make a teleconference call with the management in Vancouver and decide what to do to or generate different schedules for different possible landings or events. These decisions usually seek to only develop a feasible production plan since there is a very limited amount of time for decision-making. Our decision support tool can be very effectively used here to provide guidance to the management by showing the optimal solution for different scenarios. Since the tool works very fast one can evaluate several catch scenarios and bring the results to the meeting and present to the others. The proposed tools are capable of allocating the many fish types to many  different  products in the optimal way in seconds. We hope that this will help JSM in their daily decision-making and increase their profitability.  Moreover all the people involved in production decision can also use the  tool  separately. Each one can make her/his own reasoning and produce her/his solution and discuss these in planning meeting. The company has the necessary computer and network structure to realize these.  The cost of the implementation includes the necessary LP solver to run the decision support tool and the opportunity cost of time spent in implementation. There is no  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  40  o t h e r c o s t s e x c e p t the LP s o l v e r s i n c e t h e c o m p a n y a l r e a d y h a s M S E x c e l .  However  the benefits f r o m t h e tool a r e e x p e c t e d t o be m u c h h i g h e r t h a n t h e s e c o s t s s o in that s e n s e t h e p r o j e c t is f e a s i b l e to be i m p l e m e n t e d .  O n t h e o t h e r h a n d , the project h a s a potential t o be a c o m m e r c i a l s o f t w a r e p a c k a g e for  other  fishing  plants  a s well.  In o r d e r  to determine  the performance  of t h e  s o f t w a r e it s h o u l d be i m p l e m e n t e d a n d tried b e f o r e e x p l o r i n g o t h e r o p p o r t u n i t i e s .  4.2 SAMPLE OUTPUT As mentioned negative profits)  before  most o f the products for c h u m s (product  list o f C A T ) h a v e  profit coefficients (out o f 5 2 different p r o d u c t s o n l y 10 o f t h e m without  presented.  the by-product  information.  Here  First t h e LP s o l u t i o n with e x i s t i n g  t h e solution  profit  outputs  positive will b e  coefficients, without t h e b y -  p r o d u c t i n f o r m a t i o n , will b e s h o w n t h e n all t h e n e g a t i v e profit c o e f f i c i e n t s will b e taken  equal  to a small  profit  margin  to prevent  negative  profit  coefficients and  s o l u t i o n will b e p r e s e n t e d . V a l u e s o f all inputs ( c a p a c i t i e s , fish c o m p o s i t i o n s , profit c o e f f i c i e n t s , etc) a r e h y p o t h e t i c a l d u e to d a t a confidentiality. H e n c e t h e o u t p u t s a r e hypothetical too.  4.2.1 Sample Output with mostly negative profit coefficients T h e i n p u t s u s e d for t h e S P L P m o d e l is in T a b l e 3 : Skin Colour Ratios Amounts (lbs) Total Catch  250000  A -C  0.5  125000  Chum  0.85  212500  D -F  0.2  50000  Other  0.15  37500  G - I  0.3  75000  Size  Ratios Amounts (lbs)  Quality  Ratios Amounts (lbs)  2-4 lbs  0.1  25000  1  0.5  125000  4-6 lbs  0.4  100000  2  0.3  75000  6-9 lbs  0.3  75000  3  0.2  50000  9-12 lbs  0.2  50000  4  0  0  Canning  Ryco/HL  Filleting  Reduction  Freezing  Storage  71000  240000  85000  500000  230000  1000000  Table 3 - Sample Inputs to the SPLP Model  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  41  •  All t h e u p p e r b o u n d a r e s e t to a v e r y high n u m b e r ( 1 , 0 0 0 , 0 0 0 lbs) in o r d e r not to effect t h e LP a n d no d e m a n d (lower b o u n d s ) a r e s e t i.e. all the lower bounds are zero.  •  E s t i m a t e d r e c o v e r y rates a r e u s e d . T h e y a r e a n u m b e r b e t w e e n z e r o a n d 1 depending on product.  •  All t h e p r o d u c t s h a v e  negative  profit coefficients e x c e p t t h e following t e n  p r o d u c t s in T a b l e 4 .  Number  Product  Profit ($/lbs)  1  Ocean run fresh, 1000 lb tote  2  Frozen, type 1, 2-4 lbs, head on, red meated bright  0.2  3  Frozen, type 1, 4-6 lbs, head on, red meated bright  0.3  4  Frozen, type 1, 6-9 lbs, head o n , red meated bright  0.4  5  Frozen, type 1, 9-12 lbs, head o n , red meated bright  0.27  6  Frozen, type 1, 2-4 lbs, head off, red meated bright  0.03  7  Frozen, type 1, 4-6 lbs, head off, red meated bright  0.13  8  Frozen, type 1, 6-9 lbs, head off, red meated bright  0.26  9  Frozen, type 1, 9-12 lbs, head off, red meated bright  0.13  10  Frozen, type 1, 6-9 lbs, head off, red meated dark  0.04  0.02  Table 4 - Products that have positive profits The LP output: T h e o b j e c t i v e f u n c t i o n v a l u e is  $ 24,145  a n d t h e p r o d u c t list is: Raw Fish type Used  Amount in  Size  Skin  Quality  2 - 4 lbs  A - C  1  5312  Frozen, type 1, 2-4 lbs, head o n , red meated bright  2 - 4 lbs  D-F  1  2125  Frozen, type 1, 4-6 lbs, head o n , red meated bright  4 - 6 lbs A - C  1  21250  Frozen, type 1, 4-6 lbs, head o n , red meated bright  4 - 6 lbs  D-F  1  8500  Frozen, type 1, 6-9 lbs, head o n , red meated bright  6 - 9 lbs A - C  1  15937  Frozen, type 1, 6-9 lbs, head o n , red meated bright  D-F  1  6375  9 - 12 lbs A - C  1  10625  9 - 12 lbs D - F  1  4250  Product Type Frozen, type 1, 2-4 lbs, head o n , red meated bright  Frozen, type 1, 9-12 lbs, head o n , red meated bright Frozen, type 1, 9-12 lbs, head o n , red meated bright  6 - 9 lbs  lbs (raw fish)  Table 5 - Sample Output for the SPLP with current profit coefficients T h e total a m o u n t of fish p r o c e s s e d is 7 4 , 3 7 5 lbs. W e w e r e e x p e c t i n g this a m o u n t a n d this p r o d u c t list s i n c e all t h e p r o d u c t s a p p e a r in t h e s o l u t i o n a r e t h e p r o d u c t s w h i c h h a v e positive profit c o e f f i c i e n t s . Note that o u t of 10 p r o d u c t s f r o m T a b l e 5, 8 of t h e m a p p e a r e d in the s o l u t i o n . P r o d u c t 1 a n d p r o d u c t 10 w e r e not in t h e s o l u t i o n s i n c e t h e i r profit m a r g i n is less t h a n t h e o t h e r p r o d u c t s w h i c h c a n be u s e d to p r o d u c e  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  42  more profitable products with the same type of fish. The total available fish for the allocation was 250,000 * 0.85 = 212,500 lbs of chums. But our LP allocates only 74,375 lbs. It did not allocated the rest since the objective function goes down if it allocates for fish to other products which have negative profits. None of the capacity constraints were binding.  74,375 =,250,000, , * 0.85 Total catch  Chum Ratio  * 0.5 , , * 0.7 , Quality 1 ratio  Skin Color A-C and D-F  74,375 lbs is the amount of the total catch available to fish type of quality 1 and skin colors A - C & D-F. And only these fish types are the inputs for the profitable products.  4.2.2 Sample Output with all positive profit coefficients The same problem was solved again but this time all the negative profit coefficients were replaced with a small profit margin i.e. all the products have some profit coefficient positive. The negative profit figures were replaced with $ 0.05 / lbs. All the other outputs were the same. The solution is in Table 6.  All the available fish is allocated to end products and total production amount is 212,500 lbs. Objective function value is $ 31,051. Only canning capacity is binding (the capacity of canning is 71,000 lbs and the total amount of canned products is 71,000) but all others are not binding. The dual for capacity constraint is $ 0.24.  The chum itself has a low value but its roe is very valuable and that is the reason byproduct modeling is added into the LP model. The information about by-products and updated profit coefficient should be entered to the model before it is used.  As we  see from two sample solutions, calibration of the profit coefficients is very important and the LP solution is very sensitive to these.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  43  Raw Fish type Product Type  Size  Canned 1/4 lb  4 - 6 lbs  Canned 1/4 lb  Skin  Used Quality  Amount in lbs (raw fish)  A - C  2  12563  9 - 12 lbs A - C  2  6375  Canned 1/4 lb  2 - 4 lbs  D - F  2  1275  Canned 1/4 lb  6 - 9 lbs  D - F  2  3825  Canned 1/4 lb  2 - 4 lbs  G - I  2  1913  Canned 1/4 lb  4 - 6 lbs  G - I  2  7650  9 - 12 lbs G - I  2  3825  Canned 1/4 lb Canned 1/2 lb  6 - 9 lbs  A - C  2  9563  Canned 1/2 lb  4 - 6 lbs  D - F  2  5100  9 - 12 lbs D - F  2  2550 5738  Canned 1/2 lb Canned 1/2 lb  6 - 9 lbs  G - I  2  Canned 1 lb tall  2 - 4 lbs  A - C  3  2125  Canned 1 lb tall  4 - 6 lbs  A - C  3  8500  9 - 12 lbs A - C  Fishmeal  3  4250  Fishmeal  4 - 6 lbs  D - F  3  3400  Fishmeal  6 - 9 lbs  D - F  3  2550  Fishmeal  2 - 4 lbs  G - I  3  1275  Fishmeal  6 - 9 lbs  G - I  3  3825  9 - 12 lbs G - I  3  2550  Fishmeal Fillets, type 1  2 - 4 lbs  G - I  1  3188  Fillets, type 1  4 - 6 lbs  G - I  1  12750  Fillets, type 1  6 - 9 lbs  G - I  1  9563  9 - 12 lbs G - I  1  6375 3188  Fillets, type 1 Fillets, type 1  2 - 4 lbs  A - C  2  Fillets, type 1  4 - 6 lbs  A - C  2  188  Frozen, type 1, 2-4 lbs, head on, red meated bright  2 - 4 lbs  A - C  1  5313  Frozen, type 1, 2-4 lbs, head on, red meated bright  2 - 4 lbs  D - F  1  2125  Frozen, type 1, 4-6 lbs, head on, red meated bright  4 - 6 lbs  A - C  1  21250  Frozen, type 1, 4-6 lbs, head on, red meated bright  4 - 6 lbs  D - F  1  8500  Frozen, type 1, 6-9  lbs, head on, red meated bright  6 - 9 lbs  A - C  1  15938  Frozen, type 1, 6-9  lbs, head on, red meated bright  6 - 9 lbs  D - F  1  6375  Frozen, type 1, 9-12 lbs, head on, red meated bright  9 - 12 lbs A - C  1  10625  Frozen, type 1, 9-12 lbs, head on, red meated bright  9 - 12 lbs D - F  1  4250  Frozen, type 2, 2-4 lbs, soft, red meated bright  2 - 4 lbs  D - F  3  850  Frozen, type 2, > 9 lbs, soft, red meated bright  6 - 9 lbs  A - C  3  6375  9 - 12 lbs D - F  3  1700  3  5100  Frozen, type 2, > 9 lbs, soft, red meated bright Frozen, type 2, 2-4 lbs, firm, red meated dark  4 - 6 lbs  G - I  Table 6 - Sample Output for the SPLP with all positive profit coefficients  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  44  5.  EXTENSIONS  5.1 STOCHASTIC SUPPLY In MPMSLP model one of the assumptions was that the catch size for the next two days are known. If not, the company must enter their best guesses. Although JSM can predict the next two days catches by using information (fishing boats' contacts, weather, etc) and experience, the catch sizes will be different than the expected values due to the uncertainty involved. If the actual supply was known MPMSLP solution with actual supply would be the upper bound for the stochastic optimization solution with expected scenarios. Similarly it would be also the upper bound for MPMSLP solution with the forecasted supply.  One can expect different catch landings according to different weather, number of fishing boats or other conditions. Instead of putting one expected value for a catch landing value we can use different values with their probabilities. Another point might be doing some forecasting to provide a good estimate for JSM to help to predict the catch landings for the future days in the planning horizon.  The catch size depends on the number of boats fishing, weather conditions, duration and place of that opening and salmon in that area in that time. This suggest that the suitable forecasting method is causal forecasting since all the factors  mentioned  above are very important factors effecting the catch. Unfortunately, we did not have any data to perform this analysis but we only had times series data of total landings for 58 days effected by company policy. We performed a preliminary forecasting analysis with this existing biased data.  5.2 CATCH SIZE FORECASTING This analysis was the initial investigation for the forecasting of daily landed catch for JSM.  The data used in this analysis was daily landed catch values for the summer  salmon season of July and August 2000.  Forecasts or estimates for daily catch values over the planning horizon are necessary inputs for the MPMSLP model. At the moment this will be done by JSM using their  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  45  own judgment and the information available in that planning horizon. History and current information can be used to build a forecasting system to estimate daily catch values for J S M . One step further can be to get the catch distribution  over the  planning horizon for all fish types and qualities and this information can be used for stochastic models.  This analysis only looks at models for forecasting the total landed catch each day at JSM during the summer salmon season, which is a first step to the ultimate goal of developing forecasts of all fish types and quality levels.  The data available for this study was the daily landed catch totals for the summer salmon season July and August 2000.  In the summer salmon season for the year  2000, which lasted two months (July and August), JSM had almost 5 million lbs. of landed salmon. This period consisted of 58 days of data shown in Figure 11.  JSM Time Series of Daily Catch  Figure 1 1 - Daily Landed Catch Summer 2000  As we can see from Figure 11, there are a number of characteristics of this data series. There is not a lot of data in the data series. Fifty-eight data points is a small data set for many of the known forecasting techniques. Moreover, there is a great deal of variability in the data. deviation is 99439.  The mean of this data is 82566 and the standard  This large variability will make estimating future points very  difficult. Third, the data set contains several zero values.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  46  5.2.1 Models There are no significant correlations over time between data points, this means that the ARIMA modeling technique can not be used to provide any forecasts. As well, since no seasonality was shown, models involving seasonality should not be used. Since there is no trend pattern in the data, nor any seasonally were found in the analysis, the initial analysis involved four simple models. The "Naive 1" model (today's forecast is yesterday's actual value), simple exponential smoothing, moving average models and mean (today's forecast is the average of all observations until today).  Models were fit in-sample using all of the data. All analysis was constructed  in-sample instead of out of sample forecasts since the data series was sparse and variable.  When more data becomes available, a more extensive out of sample  comparison could be constructed.  Another attempt was the Markovian Probability model based on state transition. The question it asks is; what is the probability of moving from the given state to all other possible states in one time step? The first step for using such an approach is to describe the states that will be used for the analysis. Two states were used for this model. State x corresponds to the total landed catch, c, being equal to zero; state y to c > 0. This modeling approach has the benefit of allowing us to model the zero values directly. The next step was to derive the transition probability matrix. The transition probabilities were derived by categorizing each consecutive pair of data points as being in one of four categories: state x to state x, state x to state y, state y to state x and state y to state y. The probability estimates for each category is then derived through a simple arithmetic operation.  This is calculated by counting the  number of times that a zero value is preceded by another zero value (i.e. state x to state x) and dividing by the number of zero values.  In our data set P(C (t+1) = x | C (t) = x) = 8 / 23 = 0.348, where C(t) is total catch quantity at period t. Since the goal is to predict a value for the total daily catch, not simply predict whether or not the value is zero, a probability model for the non-zero values, state y, must be developed. A discrete probability  mass function  was  developed for the non-zero catch days using the data in the series. Simulation was the method used to test the model.  The probability model above was used to  simulate the data using the random number generator  in Excel. Two  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  random  47  numbers were used for the simulation. The first random number was used with the transition probability matrix to decide if the total landed catch was zero or larger than zero. If the catch to be larger than zero, the second random number was used with the CDF of the non-zero catch days to get a value for the total landed catch. The simulation was run 20 times and the RootMSE values for each run were averaged to determine the fit parameter for model.  5.2.2 Results All models were compared for fit on the in-sample RootMSE (Root Mean Square Error) value.  The moving average with two periods was the best fitting forecast  using this criterion and had a RootMSE of 65882. None of these time series models could produce very satisfying results. This is due to two main factors. The data reflects JSM's buying preferences and time series analysis is not very appropriate for this data; causal forecasting may perform better results. Table 7 shows RootMSE values for the models and Figure 12 shows plots for some of the models.  Method  In Sample Root MSE  Naive  142536  Simple Exp Smoothing  99692  Moving Average (2)  65882  Moving Average (3)  86309  Moving Average (5)  79999  Moving Average (10)  87127  Mean  101270  Markovian Simulation  139039  Table 7 - Summary Table of Forecasting results  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  48  350000  n  Figure 12 - Plots for total landings, mean, naive and MA (2)  5.3 STOCHASTIC If supply (amount  OPTIMIZATION of the total catch) is considered a random variable and its  distribution can be calculated using a forecasting system then, this information may be used in stochastic LP models to improve the accuracy of the model. This approach may be more successful to estimate the uncertainty  in supply. Instead of using  expected value into the deterministic model, we can use the supply distribution  to  calculate the probabilities of the expected supply values and put these into a stochastic model. However in order to have a better performance with stochastic model than the deterministic one, accurate probabilities and expected supply values are required.  5.3.1 Stochastic LP form ula tion This approach is very similar to the MPMSLP but uses multiple possible scenarios for each day in the planning horizon with the corresponding probabilities instead of expected values for the next days' supply values. At the moment we do not have data and information to calculate the probability distribution of that supply. However, JSM has already started to record more information  so that in the future this  stochastic model may be used. Note that in this proposed stochastic model, only Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  49  total catch size is assumed to be random. We assume that all other information (fish species' and quality  ratios)  is known. The scenario tree can be represented in  Figurel3.  Figure 13 - Stochastic LP Scenarios  As seen in Figure 13, there are two, three, four possible outcomes for day 1, day 2, day 3 respectively. In this model every node represents a possible catch size, scenario, for the corresponding day. From every node you can reach to any other node. These node values i.e. catch sizes and their probability of occurrence can be generated by using a forecasting system. The sum of probabilities of occurrence for each node for each day should sum up to 1. Each day a 3-day plan will be generated with the actual initial inventory values for the first day. To make the model work we need to know the inventory at the end of day 1 and day 2. However those depend on actual supply and production and we cannot determine those in advance. Instead we are approximating expectation  (expected inventory  of optimal  inventory  constraints)  those figures by taking  (7tdi) variables for s  that  particular  day  the with  probabilities of scenarios. After this initial explanation the developed model is as follows: Only new notation is given, all the others (variables, constraints, net profit calculation) are the same with revised MPMSLP model.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  50  F  = amount of type t fish from species s for product p on day d for scenario i  stpdi  S  -supply of type t fish from species son day d for scenario i  I  = deteriorated inventory of type t fish from species s on day d for scenario i  stdi  sldi  E  = expected inventory of type t fish from species s on day d for scenario i  std  B  = new inventory of type t f ish from species s on day d for scenario i  stdj  m = probability for scenario ion day d id  ^ X X X X X ^ , ^ , P I  5  id  subject to ?  ?  ^  R  F  ~" C  V  XXXX «M^ F  d  canning  vd  reduction  vd  freezer  I peR ied  s  XXXX *>M^ F  s I peF ied  XXXX^M^,/  Vd  I peE ied  s  hand line capacity  XX X H^pdi+Free^H. * I pe head on ied  XX  X s  5  X  filleting  Vd  H s,pdi^r +Free  ryco & hand line capacity  F  d  X' pe1head 1 off^ied^  d  Vd Vd  storage  t peH,E ied  X X ^'P stpdi — Uspd t ied  Vs,p,d  upper bound limit  YZ sp s,pd^D  Vs,p,d  lower bound  F  R  F  :  I ied  spd  Sstdi + sl,d-1 ~ Bstdi sldi  Vs,t,d,i supply and inventory  s di=Y. J< sjdi  Vs,t,d,i deterioriation  s,,d = X t f sldi i  Vs,t,d  expected inventory  stdi ~ stpdi P  Vs,t,d  artifical  F  +F  M  I  B  !  E  m  F  F  s,pdi. ^d  F  7  Fr  ^0  Vs, t, p, d, i non- negativity  This model is very similar to the revised MPMSLP model and the only difference is that the total catch size is not deterministic but stochastic so we can have multiple scenarios (index for scenarios is i) for the catch size and hence for production, supply and inventory for each day.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  51  5.4 A COMBINED PRODUCTION  PLAN FOR BOTH PLANTS  Developed models so far are optimizing the production for each of the two plants separately. However sometimes JSM sends fish from Prince Rupert to Vancouver to be processed in Vancouver plant. Moreover in our discussions some managers expressed they would like to know when it is optimal to send fish from north to south. This suggests that production  of both  plants plan should be prepared  together. It may be a better optimization than optimizing the plants individually and we can increase the model accuracy and reality if sending fish from North to South is a common practice. The Figure 14 represents the conceptual model idea.  Supply  Production  Supply  Production  Figure 14 - Combined Production Plan for Vancouver and Prince Rupert Transportation between the two plants is done with trucks and it takes about a day (in the model the lead time is assumed one day, but this can be adjusted if it is different). There is a fixed cost of sending trucks from north to south, independent of fish amount and variable cost dependent on fish amount. These make this new model a Mixed Integer Program (MIP). Assumptions for this MIP model are all the assumptions of revised MPMSLP model plus o  Transportation time is one day.  o  There is a fixed and variable cost for the transportation as mentioned above.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  52  5.4.1 MIP form ula tion  F  = amount of fish alocated to type t from specie s for product p on day d in city c  cstpd  V  c  = variable cost of one lbs of transporta tion from city c to the other  F  c  ~ fixed cost of transporta tion from city c to the other  C  cd  = capacity for canning on day d in city c  H  cd  = capacity for hand line on day d in city c  Y  cd  = capacity for ryco line on day d in city c  R  cd  - capacity for reduction on day d in city c  F  = capacity for freezer on day d in city c  cd  E  - capacity for filleting  S  = storage capacity on day d in city c  cd  cd  on day d in city c  S  = supply of type t fish from species s on day d in city c  I  = deteriorat ed inventory of type t fish from species s on day d in city c  csld  csld  B  = new inventory of type t fish from species s on day d in city c  csld  N  = entry of row j, column t of deteriorat ion matrix (trip deterioration)  jt  Free  cd  = free capacity for head - off products in hand line after  producing head - on products on day d in city c T  cstd  A  csld  = Departed fish from city c to other city of type t fish from specie s on day d = Arrived fish from city c to other of typet fish from specie s on day d (deteroria ted)  Z = number of trucks required on day d d  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  53  s p  t  d  c  c  s  t  d  c  d  '^  ca/i»mg  subject to X X X  «W - «/  F  C  XXX cs, d F  t  c  ^ cd  Vc,d  reduction  cst d ^ cd  Vc,d  freezer  cst d * E  Vc,d  R  P  s  V  peR  XXX s I  F  F  P  peF  XXX s t  F  P  cd  peE  XX X cs, d + s t pehead on  F  r  e  e  cd= cd  ^>  XX X cst d ^ s t pehead off  F  r  e  e  cd + cd  V>  F  P  F  P  XXX s  filleting  H  c  Y  c  cs, d ^ $cd  handline  d  d  y  R  co  & handline  Vc>d storage  F  P  t peH.E  XX 'P R  cst d ^ s d  Vs,p,dupper  bound limit  cst d ^ s d  \fs,p,dlower  bound (demand)  F  U  P  P  c t  XX s R  F  P  D  P  P  c t J cstd ^cst,d-l + A _! A = X-^/r Tcsjd Icstd X ^ i ' csjd j cstd  cstd  ^  =  - B  cstd  +F  B  y] T s t  cstd  <Z  cd  * one truck capacity  cs,d = X cst d p all variables > 0 and Z is integer F  csld  F  P  +T  V s, t, d, c supply & inv \/ c,s,t,d transporta tion deterioria tion V c,s,t,d inventory deterioria tion  cstd  \fc,d truck capacity Vc, s, t, d artifical  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  54  5.5  FURTHER  RESEARCH  There are many more and interesting opportunities for further research in this project. With the usage of the tools, JSM will be able to collect data and information about catch size, characteristics and other inputs for decision-making. And this information can be used to make more sophisticated forecasting studies and then stochastic models can be developed and used. Ground fish is another important operation of JSM as well as salmon processing. Ground fish can be caught during the year with a certain quota limit so JSM can tell fishing boats how much and when to catch and bring to the plant. In that sense the production planning for ground fish seems to be easier than salmon production scheduling but there is still room for improvement. Some research and analysis can be done for taking into account ground fish into the production planning.  The biggest problem with salmon business is that the supply is very volatile and the planning would be much more easier if it was smoother. One way to solve this problem would be reorganize the fishing boats with catch openings. Unfortunately this seems really difficult at the moment due to different parties involved in the business such as department of fisheries, fishing boats.  However, these can be  talked and negotiated with these sides and it can be rearranged for the benefit for all parties. One example can be smoothing the supply by scheduling fishing boats, giving some reward to fishing boats and deciding on how much fish to buy for processing from fishing boats.  Like in every process or production environment there is always some room for further improvement. Work and motion study can be done on production lines, the process and work flow charts can be prepared, bottleneck resources are determined and some improvement can be done.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  55  6.  CONCLUSION  The focus of this study was to develop an effective and efficient method for JSM to allocate daily a fresh salmon harvest between the various products they produce on a daily basis. The goal is short-term production planning, to allocate the catch among the products in such a manner that the profit potential of the catch is maximized. Automation of the decision making process for the catch allocation, "what if" planning, decreasing expert dependency, reducing decision making time, and building a practical and innovative decision support tool are the additional goals. In order to solve this problem efficiently and effectively, several optimization models were developed for allocating the catch to the end products.  For one of the models (SPLP), the corresponding decision support tool was built for the end-users at JSM and it is ready for implementation. A decision support tool for more complex models can be built if required.  Implementation involves presentation and installation of the decision support tool and giving user support. Our implementation recommendation to JSM is to start using the software for a trial period concurrently with the company's current system. This would help JSM to gain familiarity with the tool and would enable us to compare the performance of the tool with the company's current system over some time period. The quality of the solution depends on inputs provided therefore there may be a need for inputs calibration before using the tool. We hope that this study will help JSM in their daily decision-making and increase their profitability.  On the other hand, the project has a potential to be extended to a commercial software package for other fishing plants as well. In order to see the performance of the software it should be implemented and tried before doing so.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  56  REFERENCES •  Web  sites  of  Fisheries  and  http://www.ncr.dfo.ca/index.htm •  Oceans and  and  BC  fisheries  web  sites.  http://www.qov.bc.ca/fish  Laura Jean Morrison, Decision Making in the Fisheries Industry, MSc. Thesis, University of British Columbia, Canada, 2000.  •  S. Makradakis, S. Wheelwright and R. Hyndman, Forecasting Methods and Applications, 3rd Edition, John Wiley and Sons, New York, 1998.  •  O.I. Forsberg, Optimal stocking and harvesting of size-structured farmed fish: A multi-period linear programming approach, Mathematics and Computers in Simulation 42 (1996) 299 - 305  •  E.A. Gunn, H.H. Millar, S.M Newbolt, Planning harvesting and  marketing  activities for integrated fishing firms under an enterprise allocation scheme, European Journal of Operations Research 55 (1991), 243-259. •  S . U . Randhawa, E.T. Bjarnason, A decision aid for coordinating fishing and fish processing, European Journal of Operations Research 81 (1995) 62-75.  •  Begen  Mehmet,  Puterman  Martin,  Optimal  Catch  Allocation,  Mitacs  Newsletter, Summer 2001, 8 •  Web  site  of  Frontline  Systems  for  Large  Scale  LP  solver  http://www.frontsvs.com or http://www.solver.com  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  57  APPENDIX l - SUMMARY OF THE PRODUCTION PROCESS  Fishing boats arrive to the plant after and during the catch openings  The landed fish is unloaded from boats using a pump to a temporary waiting space,tank  >  Cannery: fish is dressed, cleaned, cut, canned, cooked.  I  Fish is prepared for shipping or storing  Hand line: fish is dressed and cleaned.  J  Fish is stored temporarily before going to grading in this tank  Ryco line: fish is dressed and cleaned.  Reduction Plant: all parts of fish not used in production are boiled, oil & other parts are separated  V  Leftovers from manufacturing from other companies for the reduction plant  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  Products: Fish Meal and fish oil.  58  APPENDIX  2 - FISH CATEGORIES AND PRODUCT LIST FOR CHUMS  There are 48 types of fish in the SPLP model. Four sizes, 3 skin colour categories and four quality levels. A - C refers to finest skin colour and 1 is the best quality level.  Size  Skin Colour  Quality  2 - 4 lbs  A - C  1  4 - 6 lbs  D-F  2  6 - 9 lbs  G - I  9 - 12 lbs  3 4  There are 52 different products. Main categories are can, fresh, frozen, fillets and fishmeal. The complete product list as follows: Canned 1/4 lb  Frozen, type 2, 6-9 lbs, firm/soft, red meated bright  Canned 1/2 lb  Frozen, type 2, 9-12 lbs, firm/soft, red meated bright  Canned 1 lb tall  Frozen, type 2, 2-4 lbs, firm, red meated bright  Canned 4 lb  Frozen, type 2, 4-6 lbs, firm, red meated bright  Canned fr. Fillet, 1/4 pulltop  Frozen, type 2, 6-9 lbs, firm, red meated briqht  Freeze for A pulltop  Frozen, type 2, 9-12 lbs, firm, red meated briqht  Fishmeal  Frozen, type 2, 2-4 lbs, soft, red meated briqht  Ocean run fresh, 50 lb pkq  Frozen, type 2, 4-6 lbs, soft, red meated briqht  Ocean run fresh, 1000 lb tote  Frozen, type 2, 6-9 lbs, soft, red meated briqht  Bright fresh, 50 lb pkg Briqht fresh, 1000 lb tote  Frozen, type 2, 9-12 lbs, soft, red meated briqht Frozen, type 1/2, unsized, firm/soft, pale meated bright/dark  Fillets, type 1  Frozen, type 2, unsized, firm/soft, red meated dark  Fillets, type 2  Frozen, type 2, < 9 lbs, soft, red meated bright  Fillets, canned  Frozen, type 2, < 9 lbs, firm, red meated briqht  Fillets, pale  Frozen, type 2, > 9 lbs, soft, red meated bright  Fillets, scrap  Frozen, type 2, > 9 lbs, firm, red meated bright  Frozen, type 1, 2-4 lbs, head o n , red meated briqht  Frozen, type 1, 2-4 lbs, head off, red meated dark  Frozen, type 1, 4-6 lbs, head on, red meated bright  Frozen, type 1, 4-6 lbs, head off, red meated dark  Frozen, type 1, 6-9 lbs, head o n , red meated briqht  Frozen, type 1, 6-9 lbs, head off, red meated dark  Frozen, type 1, 9-12 lbs, head o n , red meated briqht  Frozen, type 1, 9-12 lbs, head off, red meated dark  Frozen, type 1, 2-4 lbs, head off, red meated briqht  Frozen, type 2, 2-4 lbs, firm, red meated dark  Frozen, type 1, 4-6 lbs, head off, red meated briqht  Frozen, type 2, 4-6 lbs, firm, red meated dark  Frozen, type 1, 6-9 lbs, head off, red meated briqht  Frozen, type 2, 6-9 lbs, firm, red meated dark  Frozen, type 1, 9-12 lbs, head off, red meated bright  Frozen, type 2, 9-12 lbs, firm, red meated dark  Frozen, type 2, 2-4 lbs, firm/soft, red meated briqht  Frozen, type 2, < 9 lbs, firm, red meated dark  Frozen, type 2, 4-6 lbs, firm/soft, red meated bright  Frozen, type 2, > 9 lbs, firm, red meated dark  1  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  59  APPENDIX 3 - PRINCE RUPERT'S PLANT  HIGHLIGHTS  Fishing boats, either waiting for unloading or resting after unloading  This is the pump used for unloading, on the top ice unit and on the right cannery  Initial grading to different totes  Plant, outside  Ryco Line, machine cut the heads off automatically  Hand Line, every operation is done by hand.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  60  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  61  Cannery, fish is dressed and cleaned  Cannery, fish is put to the cans  Cannery, canning machine  Cannery, canned fish is being cooked  Roe, is prepared and boxed  Salmon Roe  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  APPENDIX  4 - AMPL  MODEL  A M P L prototype of the model: # index definitions set type; set family; set product; set day;  # parameter definitions param profit {i in type, f i n family, j in product, k in day}; param capacity { f i n family, k in day}; param demand (j in product, k in day}; param upperbound (j in product, k in day}; param totalcatch {i in type};  # decision variable definitions var x {i in type, f i n family, j in product, k in day};  # objective function maximize totalprofit: x[i,fj,k]*profit[i,f,j,k];  sum{i  in  type,  f  in  family, j  in  product,  k  in  day}  # constraints subject to capacities { f i n family, k in day}: sum{i in type, j in product} x[i,f,j,k] <= capacity [f,k]; subject to demands {j in product, k in day}: sum{i in type, f in family} x[i,fj,k] >= demand[j,k]; subject to upperbounds {j in product, k in day}: sum{i in type, f in family} x[i,f,j,k] <= upperbound[j ,k]; subject to totalsupplys {i in type}: sum{f in family, j in product, k in day} x[i,f,j,k] <= totalcatch[i]; subject to positive {i in type, f i n family, j in product, k in day}: x[i,f,j,k]>=0;  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  64  APPENDIX  GETTING  5-CAT  DESCRIPTION  STARTED  HO W THE APPLICA TION STARTS? •  The Catch Allocation Decision Tool is accessed by opening CAT.XLS.  •  A dialog box will appear, warning that CAT.XLS contains macros. Select the "Enable Macros" option.  NA VIGATION OF THE TOOL After opening the tool, it checks the demand and upperbound values and gives a warning message if there are any. And the user will press ok button to continue. This warning is aimed to get the attention of the user for the existing demand and upperbound limits.  Upper Bound Limit Info There are some upper bounds limits please check OK  Then main menu appears and it is ready to be used. The main menu has 4 different submenus (step 1, step 2, step 3 and step 4) and 4 buttons (help, about, save and exit). The user will go through from step 1 to step 4, enter the necessary inputs (step 1, step 2, step 3) to the program and at the end (step 4) will solve the linear program and get the production schedule that maximizes the expected profit for the planning horizon. These steps are explained with screen views in the rest of this document. The necessary explanations are also presented on the tool.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  65  STEPI  (CATCHCHARACTERISTICS)  The user will enter the catch properties and can save this to the catch history and view the previous history. The corresponding buttons can be seen above: catch properties, save history, view history buttons. When the user press these buttons the corresponding forms or sheets are opened and the user can enter or view the desired information.  The submenus (step 1, step 2, step 3, step 4)  he Chum Salmon Allocation Decision Tool *epi jstejfe ) step/1 scep4 | Catch Properties View and Update the Catch Properties  Catch Properties  Opens the catch > properties form to view, modify  Catch Properties History Save the History Total Catch and Catch Properties  Save History  Save current catch properties to the history sheet  Catch Properties History Save the Kstory Total Catch and Catch Properties  HELP  Opens help file (this file)  View History  About  Save  Contact Info for support  Save the file  Opens the history sheet for viewing  Exit  Exit the program without saving  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  66  REVENUE)  STEP2 (CAPACITY,  In this screen the user will enter the plant properties as inputs to the program such as cost and revenue figures and capacity figures. The buttons for these inputs can be seen below.  The Chum Salmon Allocation Decision Tool  stepl  step2 tep3 step4 s  r— Product Costing and Revenue ! View/Adjust Costing & Revenue Figures  View Cost  - P r o c e s s i n g Line C a p a c i t y One Shift Only  View/Adjust Capacity One Shift Only  View Capacity  r Processing Line Capacity All the Shifts Total — | View/Adjust Capacity Total Capacity  View Capacity  Frozen Storage Capacity View/Adjust Capacity  HELP  About  View Storage  Save  Exit  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  Opens the Cost sheet for viewing, modifying Opens the Capacity form "Tor viewing, modifying Opens the > Total capacity form for viewing or modifying Opens the Storage capacity form for viewing or modifying  67  STEP3 (UPPERBOUNDS, In  this  screen the  ORDERS, user  will  RECOVERY) enter  the  maximum  desired  production  levels  (upperbounds), minimum desired production levels (orders) and recovery rates. The buttons for these can be seen below. The Chum Salmon Allocation Decision Tool stepl  2<J  step2 step3 step4  Upperbound View/Adjust upperbound  view upper bounds  Opens the Orders (demand) sheet for viewing, modifying  Product M a r k e t C o m m i t m e n t s  View/Adjust Market Orders  Opens the UpperBound sheet for viewing, modifying  View Orders  Round to-Processed Lb Recovery Rates  View/Adjust Recovery Ratios  HELP  About  View Recovery  Save  Opens the Recovery sheet for viewing, modifying  Exit  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  68  REPORTING)  STEP4 (SOLUTION,  In this screen the user will get the solution of the LP and its reports. There are 4 different parts (frames) on step 4 submenu. The explanations are presented below:  2U  The Chum Salmon Allocation Decision Tool stepl | step2 | step3  step4  r~ Solution •—-——-— Allow Second Shift ^iNoi  Solution Reporting View Recommendation Report"  r yes  Generate New Allocation Plan Generate a new plan  ff dayl r day2  C day3  view the solution report |  Aggregate Reports Aggregate Production  fL dayl C day2 (~ day 3  Aggregate Production  Products It dayl C day2 C day3  HELP  Allows the user to view the solution reports.  About  Products  Save  Exit  In d a y l ' s report there is a tool that allows the _^.user to make some what-if analysis. The user will select a day and press the view solution report button. Takes the reports of the "solution reporting" and generates a report " ^ e l i m i n a t i n g the zero production levels. Takes the reports of the "aggregate ^production" and generates a report showing the different products recommended by the LP solution.  This frame solves the LP and produces the solution. The user will choose the number of ^ . shifts (one or more, default value is one shift) and then will press to generate a new plan button. When the user hits this button the program checks the feasibility conditions and gives a warning message with an action recommendation if these are not satisfied. When the user chooses more than one shift the program asks to verify the total capacity settings again to prevent any confusion or mistakes.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  69  SOL VER MESS A GE  stepl | step2 | step3  step4 |  Solution  Solution Reporting  Allow Second Shift <• No  View Recommendation Report  C yes  Generate New Allocation Plan Generate a new plan  dayl r day2  f day 3  view the solution report  Solver Results  JLJ*]  Solver found a solution. All constfaints and optimality conditions are satisfied.  Reports Answer Sensitivity Limits  <*• i^^^^^Sdiution] <~ Restore Original Values OK  Cancel  HELP  About  3J _J  Save Scenario...  Save  Help  Exit  This pop-up menu appears after pressing to the "generate a new plan" button. This is generated by the LP solver and gives information about the solution. When this pop-up menu appears as it is here ("Solver found a solution and all constraints and optimally conditions are satisfied") means the optimal solution is found. The user will press OK and return to main menu. Sometimes the solver cannot find a solution because of infeasibilities. If this is the case, the user must check the supply, demands and capacity figures and must verify these. In order to provide more guidance to users tool checks capacities and demands (2 possible infeasibility causes) before solving LP and warns user with corresponding message if there are any feasibilities.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  THE FORMS AND SHEETS IN THE TOOL: THE CATCH PROPERTIES  MENU  When the user press the catch properties button on step 1 submenu, this form is opened. The form takes the current values for these estimates and user can view these then the user will enter the catch properties using this menu. User must press the corresponding  update  button after entering  the  new values to  modify  the  estimates. And if the value entered by the user is not correct the program will pop up a warning message and recommend an action. (For example the values should sum up to 1 for each frame estimates) Catch Properties • Current Catch Volumes : Day 1  . . .  ••  1  * _ _ • *_ „ * Size of Catch (in raw lbs)  Current Catch Estimates Current catch  •'  Composition Estimates Chum  '  Day i : : : : |  Pink :::: |  —  update estimates  used to view, enter the ^. catch volume used to  »'••»••••-••• ni'i-rn > • • •  •  ..::::::::::::::::::::::::: Spring: : : : coho Other update estimates • •' '• '• ' . ' • • —'•— • • I  •  .v.j  '. '.  "  • Cumnt Sizt EstirrutM . . . • . • Current size estimates Size Estimates •  Gurwnt Skin Colour Estimates  Current skin colour  -  Skin Colour Estimates D-F A-C  G-I  update Estimates  Day 1  Cun^nt Quality Estimate* -  •' Current quality  View, enter the Catch no r sition cmo m p used to view, enter the size estimates  used to view, enter the skin colour.  Quality Estimates  Quality 1  Quaky 2  Quality 3  Quality 4  update estimates  Day 1  used to view, enter the quality estimates  This button is used to go back to main menu. The program will check if there is any change on this form that is not updated (i.e. the user did not press the update estimates button) and will give a warning message with a recommended actions.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  71  VIEW THE  HISTORY  History of Catch Characterictics In this sheet you can view and print old catch properties with the date and time The amounts are for all types of species.  ChumPink i Total Catch  1  100000  85000  15000  in lbs (Raw Fish)  CohoSpring OtherSize 2-4 lbs 4-6 lbs 6-9 lbs 9-12 lbs 0  0  0  30000  20000  3  4  20000  30000  4/27/2001 10:11  Skin Colour A-C D-F 40000  300D0  G-I Qualify/  30000  80000  2  15000  5000  0  Total Catch Chum Pink CohoSpring OtherSite 2-4 lbs 4-6 lbs 6-9 lbs 9-12 lbs 100000 85000 15000  0  0  0  30000  20000  3  4  20000  30000  4/27/2001 10:13  Skin Colour A-C D-F 40000  30000  G-I Qualify/  30000  80000  2  15000  5000  0  Total Catch Chum Pink CohoSpring OtherSize 2-4 lbs 4-6 lbs 6-9 lbs 9-12 lbs 100000 85000 15000 0  0  0  30000  20000  3  4  20000  30000  4/27/2001 11:08  Skin Colour A-C D-F 40000  30000  G-I Qualify/  30000  80000  2  15000  5000  0  The saved (it can be saved by pressing save history button from s t e p l submenu) history can be viewed from this sheet by pressing view history button from s t e p l submenu.  Go back to main menu  VIEW CAPACITY (ONE SHIFT), VIEW CAPACITY (ALL SHIFTS), VIEW STORAGE Since the usage of all these are similar only one of them is presented and explained here. Current Capacity Settings Available Processing Line Capacities (in raw lbs) • Update Capacity Settings <lbs raw fish) Day  1  Day  2  Dfl  Canning R y C 0 / H a n d L k i e  I;!  :  :  Filleting  y  3  : : :  In this form : : : ; [ ' : : : : the capacity :::::[.:::; information for the productions : : : : : : : : : lines can be viewed and updated.  Reduction Freezing - Internal Freezing - Rented •  update settings  mam menu  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  72  VIEW COST, VIEW UPPER BOUNDS ,BY-PRODUCTS INFORMATION VIEWRECOVERY  VIEW ORDERS,  Since the usage of all these are similar only one of them is presented and explained here.  Costing & Pricing Entry in lbs (Processed Units)  Bright fresh, 50 lb pkg  Bt-Produa 1 (Rot StVSO Kl  Selling Price Purchase Price Labour Expense Packaging Expense Miscelaneous Expenses NET Profit  After updating the cost figures, the user will press this button for the update costing information in the LP.  \  \  Press this button to return back to main menu. In this sheet and in other sheets which has an update feature, the program checks and gives a warning message if there is any change.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  73  SOL UTION REPOR TING This is for the first day and since the others are very similar only this is presented here. (The others have main menu button only) Allocation Results Report: Day 1 CAMMED I  Update Prof*  I '.lim.il.' I Profit (for entire planning \ Drizon)  Get LP Solution Back  CAMMED |  CAMMED  I  CAMMED  I  CAMMED  |  Canned fr Canned 1 /4 lb Canned 112 to Canned 1 lb tall Conned 4 to Filet, 1/4 puHtop  CAMMED  | FISHMEAL  Freeze tor 1H pulitop  f  FRESH  Fishmeal  Ocean run tresh.SOIb pkg  0 0  0 0  0 0  0 0 0  Ocean n tresh, 100 tote  Skin Color Quakl 0\  0  o \ N  0 0 0  0  \  0  \ l \  0 X  0 0  0  \ 0 \\  0 0  0 0  0  \ \  0  0  L ">0  X  0  0  \ 0\  0  0 0 0 0 0  0 0  \\  \ >V  \  0 0 0  0 0  \ \  0  D  0 0  V  0 0 0  o\ 0  Returns back main menu  0 0 0 0  0  V  0  0  0  0 \ 0 \ 0 0 0 0  \  \  0  0  0 0 0  0 0 0  0 0 0  0 0_ 0 0  0 0 0 0  0  0  0  0 0  0 0 0 0 0 0  0 0 0 0  0 0  0 0 0  0  V  t Expected profit of the LP solution  0 0  0  Returns back to the original LP solution  o 0  o\ 0 0 0 0 0 0 0  a 0 a  \  \\  0 0  °\ 0 0 0_ 0  0 0 0 0  0 0 0 0 0  0  0  0  \  0 0 0 0  \\ o  0 0 0  0 0 0  0 0 0 0 0 0 0  0 0 0 0  0 0 0  0 0 0  0  0 0 0  0 0 0 0 0 0  0  0  0  0  0 0 \  0 0 0 0 0  0 D 0 0  o 0  0 0  0 0 0 0 0 0  0  The user can modify the production quantities on this report and then see the expected profit of this change by pressing update profit button. But do not forget this can be infeasible.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  74  A GGREGA TE REPOR TS This is for the first day and since the others are very similar only this is presented here. X Microsoft EMtet - jsmjh Efc E « Set* Insert Fo/rnat look Beta  *  &a  • 10  ST  J_  tjMon  - B / u E • -  Type of the product to be produced  Help  zi J ,  m m * % , tog  Type of the raw fish to be used  m#  *J  .5 tr *  100% . g  • .  a #  _ - * - ti •  Amount of the raw fish to be used  Back to main menu Expected Profit  This report takes the previous report (above) and identifies only the production schedule and generates the production schedule.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  nonzero  75  PRODUCTS  REPORT  This is for the first day and since the others are very similar only this screen is presented here. -|g|x  *y G>a Edit Wew Insert Formet loot! Djta Wn idow Help Dc*He5Eiy*%i®<^ * * * A ii JU „ Tm i es New Roman -11 • B / U 9 3 gl S % , *j8 .i8  -tal* #  *  loo* -  $  if- _ - * • ^ C D E Expected Protit for the enote hori2on  Dayl Production  H  I  -  ±1 5  Pioditct Type  7  there are 5 different products proposed  8 9 11 Frozen, type 1, 2-4 lbs. head on, red miated bright 12 Frozen, type 1, 4-6 lbs, head on, red meated bright 13 Frozen, type 1, 6-9 lbs, head on, red meated bnght 14 Frozen, type 1, 9-12 ibs, head on, red meated bright 15 Frozen, type 1, 4-6 Ibs, head off, rea meated bright 16 17 18 19 20 21 22 23 | 24 25 26 27 2B  |l<  i  • M  •r  I-I  Type of the product to be produced  Amount of the raw fish to be used  Expected Profit  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  Back to main menu  76  APPENDIX  6 - MPMSLP  PROTOTYPE  Supply | initial Inventory | deterioration | Profit | Recovery Rates ] Qa LB, UB ] total supply  dayl  10000I  day2  day3  25000  15000  Supply Information  dayl  day2  day3  chum  0.2  0.4  0.2  pink  0.3  0.2  0.5  | 0.5  | 0.4  | 0.3  quality 1  quality2  quality3  | 0.2  | 0.25  | 0.55  day2  | 0.35  | 0.5  | 0.15  day3  | 0.35  | 0.25  | 0.4  quality 1  quality2  dayl  | 0.2  | 0.8  day2  | 0.5  | 0.5  day3  | 0.35  | 0.65  sockeye Chum Composition dayl  Pink Composition  Catch Allocation Tool Update Info in the Model  Generate a new plan  View the plan Sockeye Composition quality 1  sizel  size2  dayl  | 0.2  | 0.8  | 0.15  | 0.85  day2  | 0.4  | 0.6  | 0.65  1 0.35  day3  | 0.8  | 0.2  | 0.6  | 0.4  quality2  Savetk Exit  Screen example from the prototype of the new MPMSLP model.  Begen, Production Planning in JS McMillan: Catch Allocation Tool Design  77  

Cite

Citation Scheme:

        

Citations by CSL (citeproc-js)

Usage Statistics

Share

Embed

Customize your widget with the following options, then copy and paste the code below into the HTML of your page to embed this item in your website.
                        
                            <div id="ubcOpenCollectionsWidgetDisplay">
                            <script id="ubcOpenCollectionsWidget"
                            src="{[{embed.src}]}"
                            data-item="{[{embed.item}]}"
                            data-collection="{[{embed.collection}]}"
                            data-metadata="{[{embed.showMetadata}]}"
                            data-width="{[{embed.width}]}"
                            async >
                            </script>
                            </div>
                        
                    
IIIF logo Our image viewer uses the IIIF 2.0 standard. To load this item in other compatible viewers, use this url:
http://iiif.library.ubc.ca/presentation/dsp.831.1-0090086/manifest

Comment

Related Items