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Adaptive optimization methods to improve the performance of chop saw systems Ye, Lin
Abstract
Optimizing chop saw is one of the primary methods for adding value to rough lumber (boards) and it is common to many breakout lines for plants processing solid wood. Because of the variety of boards to be chopped in real time and the diversity of parts (products) required on a cutting list, there are numerous ways of cutting the boards. The purpose of this thesis was to advance the optimization algorithms that can be used in real-time to generate optimal cutting patterns, aiming to minimize the overall cost of the chop saw operation. Adaptive methods were used in the models to acquire the knowledge on the quality of boards and the inventory level for each part in real-time. The first part of the project developed a real time goal-seeking model for chop saw optimization systems that can be implemented as an on-line control system. Two prioritizing strategies were used in the model by applying an adaptive method that can link the part value with the production level for each part during the production process. A simulation study was conducted to compare the performance of the goal-seeking model with a published model. The result shows that by selecting a proper prioritization strategy, the goal-seeking model can achieve a lower level of overproduction, higher part yield and lower overall cost. The study also found that the goal-seeking model balances the production rates for each part on the cutting list over the production run. The second part of the project developed a combined linear programming and dynamic programming model that used adaptive optimization methods. This model considers the part size and quantity in the cutting list and the grade of boards being processed and the production level for each part chopped in real-time. The simulation study shows that, different from goal-seeking model that requires selecting a proper prioritization strategy for a given cutting list, the combined model has a steadier control to overproduction and good performance against different cutting lists. However, the result is sensitive to the overproduction penalty assigned to the model.
Item Metadata
Title |
Adaptive optimization methods to improve the performance of chop saw systems
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
2004
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Description |
Optimizing chop saw is one of the primary methods for adding value to rough
lumber (boards) and it is common to many breakout lines for plants processing solid
wood. Because of the variety of boards to be chopped in real time and the diversity of
parts (products) required on a cutting list, there are numerous ways of cutting the boards.
The purpose of this thesis was to advance the optimization algorithms that can be
used in real-time to generate optimal cutting patterns, aiming to minimize the overall
cost of the chop saw operation. Adaptive methods were used in the models to acquire the
knowledge on the quality of boards and the inventory level for each part in real-time.
The first part of the project developed a real time goal-seeking model for chop saw
optimization systems that can be implemented as an on-line control system. Two
prioritizing strategies were used in the model by applying an adaptive method that can
link the part value with the production level for each part during the production process.
A simulation study was conducted to compare the performance of the goal-seeking
model with a published model. The result shows that by selecting a proper prioritization
strategy, the goal-seeking model can achieve a lower level of overproduction, higher part
yield and lower overall cost. The study also found that the goal-seeking model balances
the production rates for each part on the cutting list over the production run.
The second part of the project developed a combined linear programming and
dynamic programming model that used adaptive optimization methods. This model
considers the part size and quantity in the cutting list and the grade of boards being
processed and the production level for each part chopped in real-time. The simulation study shows that, different from goal-seeking model that requires selecting a proper
prioritization strategy for a given cutting list, the combined model has a steadier control
to overproduction and good performance against different cutting lists. However, the
result is sensitive to the overproduction penalty assigned to the model.
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Extent |
6506363 bytes
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Genre | |
Type | |
File Format |
application/pdf
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Language |
eng
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Date Available |
2009-11-21
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Provider |
Vancouver : University of British Columbia Library
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Rights |
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.
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DOI |
10.14288/1.0075012
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
2004-05
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Campus | |
Scholarly Level |
Graduate
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Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.