Multi-objective optimization for road vertical alignment design
department or school: Science, Irving K. Barber Faculty of (Okanagan)
department or school: Computer Science, Mathematics, Physics and Statistics, Department of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Akhmet, Ayazhan
issued: 2021-12-25T08:00:00Z
issued: 2021
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: This thesis defines a multi-objective optimization model that seeks to find road profiles that would be optimal for the manufacturers in terms of the road construction cost and at the same time for the users in terms of the vehicle operating costs, specifically in terms of the fuel consumption cost. The research implements and validates the formula for the fuel consumption cost. It further presents and examines a variety of well-known methods: three classical scalarization techniques (the ε-constraint method, weighted sum method, and weighted metric methods) and two widely-used evolutionary methods (NSGA-II and FP-NSGA-II). Moreover, to accelerate the performance of the chosen scalarization approaches, a warm start strategy is proposed. Numerical experiments are performed on 30 road samples for Caterpillar 793D off-highway trucks to determine the most robust approach for the proposed multi-objective optimization problem. The results are analyzed using the commonly-used performance indicators, namely, hypervolume (to assess the convergence of solutions), spacing (to assess the diversity of solutions), and CPU time (to assess the speed). The research determines that the most promising and recommended method for the proposed problem is the ε-constraint method (successfully solved approximately 75% of test problems) followed by the weighted sum method (successfully solved approximately 50% of test problems). Moreover, the research shows that the warm start strategy improves the performance of the scalarization techniques.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/78928
note: Multi-Objective Optimization forRoad Vertical Alignment DesignbyAyazhan AkhmetB.Sc., Nazarbayev University, 2019A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinTHE COLLEGE OF GRADUATE STUDIES(Computer Science)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)June 2021© Ayazhan Akhmet, 2021The following individuals certify that they have read, and recommendto the College of Graduate Studies for acceptance, a thesis/dissertation en-titled:Multi-Objective Optimization for Road Vertical AlignmentDesignsubmitted by Ayazhan Akhmet in partial fulfilment of the requirementsof the degree of Master of ScienceDr. Warren Hare, Irving K. Barber School of Arts and ScienceSupervisorDr. Yves Lucet, Irving K. Barber School of Arts and ScienceSupervisory Committee MemberDr. Solomon Tesfamariam, School of EngineeringSupervisory Committee MemberDr. Amir Ardestani-Jaafari, Faculty of ManagementUniversity ExamineriiAbstractThis thesis defines a multi-objective optimization model that seeks tofind road profiles that would be optimal for the manufacturers in terms ofthe road construction cost and at the same time for the users in terms ofthe vehicle operating costs, specifically in terms of the fuel consumptioncost. The research implements and validates the formula for the fuel con-sumption cost. It further presents and examines a variety of well-knownmethods: three classical scalarization techniques (the ε-constraint method,weighted sum method, and weighted metric methods) and two widely-usedevolutionary methods (NSGA-II and FP-NSGA-II). Moreover, to acceler-ate the performance of the chosen scalarization approaches, a warm startstrategy is proposed.Numerical experiments are performed on 30 road samples for Caterpillar793D off-highway trucks to determine the most robust approach for the pro-posed multi-objective optimization problem. The results are analyzed usingthe commonly-used performance indicators, namely, hypervolume (to assessthe converge…
has type: Thesis/Dissertation
date issued: 2021-09
is shown at: 10.14288/1.0400118
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Hare, Warren
type: Text
identifier URI: http://hdl.handle.net/2429/78928