A hybrid multi-objective evolutionary algorithm for solving truck and trailer vehicle routing problems

• Published in: European journal of operational research Vol. 172; no. 3; pp. 855 - 885
• Main Authors: Tan, K.C., Chew, Y.H., Lee, L.H.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.08.2006; Elsevier; Elsevier Sequoia S.A
• Series: European Journal of Operational Research
• Subjects: Algorithms; Applied sciences; Decision theory. Utility theory; Evolutionary algorithms; Exact sciences and technology; Flows in networks. Combinatorial problems; Genetic algorithms; Heuristic; Job orders; Logistics; Multi-objective optimization; Operational research and scientific management; Operational research. Management science; Operations research; Optimization; Route optimization; Studies; Transportation problem (Operations research); Trucks; Vehicle routing; Vehicles; Vehicle routing; Multi-objective optimization; Evolutionary algorithms; Local search; Pareto optimum; Decision support system; Decision making; Evolutionary algorithm; Vehicle routing problem; Multiobjective programming; Combinatorial optimization; Optimization; Container; Transportation problem; Time window; Minimal distance; Logistics
• ISSN: 0377-2217; 1872-6860; 1872-6860
• DOI: 10.1016/j.ejor.2004.11.019

This paper considers a transportation problem for moving empty or laden containers for a logistic company. Owing to the limited resource of its vehicles (trucks and trailers), the company often needs to sub-contract certain job orders to outsourced companies. A model for this truck and trailer vehicle routing problem (TTVRP) is first constructed in the paper. The solution to the TTVRP consists of finding a complete routing schedule for serving the jobs with minimum routing distance and number of trucks, subject to a number of constraints such as time windows and availability of trailers. To solve such a multi-objective and multi-modal combinatorial optimization problem, a hybrid multi-objective evolutionary algorithm (HMOEA) featured with specialized genetic operators, variable-length representation and local search heuristic is applied to find the Pareto optimal routing solutions for the TTVRP. Detailed analysis is performed to extract useful decision-making information from the multi-objective optimization results as well as to examine the correlations among different variables, such as the number of trucks and trailers, the trailer exchange points, and the utilization of trucks in the routing solutions. It has been shown that the HMOEA is effective in solving multi-objective combinatorial optimization problems, such as finding useful trade-off solutions for the TTVRP routing problem.