Open Collections

Akhmet, Ayazhan

University of British Columbia

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.

Text

2021-12-25

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/78928

Computer Science

University of British Columbia

UBCO

http://creativecommons.org/licenses/by-nc-nd/4.0/