Hybrid multiobjective evolutionary algorithm with fast sampling strategy-based global search and route sequence difference-based local search for VRPTW

作者：

Highlights：

• HMOEA-GL combines FSS-GS and RSD-LS.

• FSS-GS quickly explores the entire solution space.

• RSD-LS further enhances the search ability of HMOEA-GL.

• Designing suitable coding method and proper genetic operators.

• Simple insertion search is used to reduce the number of vehicles.

摘要

•HMOEA-GL combines FSS-GS and RSD-LS.•FSS-GS quickly explores the entire solution space.•RSD-LS further enhances the search ability of HMOEA-GL.•Designing suitable coding method and proper genetic operators.•Simple insertion search is used to reduce the number of vehicles.

论文关键词：Multiobjective evolutionary algorithm,Vehicle routing problem,Global search,Local search,Transportation

论文评审过程：Received 18 August 2019, Revised 3 December 2019, Accepted 19 December 2019, Available online 20 December 2019, Version of Record 27 December 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.113151