A hybrid multiobjective evolutionary algorithm for solving truck and trailer vehicle routing problems

Author

Tan, K.C. ; Lee, T.H. ; Chew, Y.H. ; Lee, L.H.

Author_Institution

Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore

Volume

3

fYear

2003

fDate

8-12 Dec. 2003

Firstpage

2134

Abstract

This paper considers a transportation problem for moving empty or laden containers for a logistic company. 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 and multimodal combinatorial optimization problem, a hybrid multiobjective evolutionary algorithm (HMOEA) is applied to find the Pareto optimal routing solutions for the TTVRP. Detailed analysis is performed to extract useful decision-making information from the multiobjective optimization results. The computational results have shown that the HMOEA is effective for solving multiobjective combinatorial problems, such as finding useful trade-off solutions for the TTVRP.

Keywords

evolutionary computation; optimisation; road vehicles; transportation; Pareto routing; combinatorial optimization; decision-making information; multimodal optimization; multiobjective evolutionary algorithm; optimal routing; routing distance; routing schedule; transportation problem; truck and trailer vehicle routing problem; Availability; Constraint optimization; Containers; Evolutionary computation; Logistics; Pareto optimization; Routing; Time factors; Transportation; Vehicles;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2003. CEC '03. The 2003 Congress on

Print_ISBN

0-7803-7804-0

Type

conf

DOI

10.1109/CEC.2003.1299936

Filename

1299936