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Journal of System Simulation

Abstract

Abstract: In order to provide reasonable and effective decision support for logistics enterprises in vehicle distribution route planning, a hybrid evolutionary multi-objective optimization algorithm combining a multi-region mixed-sampling strategy for global search and a local search based on individual route sequence differences is proposed for the problem. A reasonable mathematical model is constructed and the global search strategy is used to make the population individuals to converge quickly to the Pareto front from multiple directions, and the local search strategy is employed to guide the poorly performing individuals in the population to evolve towards the direction of better performing individuals, thus improving both individuals quality and local search capability of the algorithm. By conducting a series of experiments on a standard benchmark of vehicle routing problem with simultaneous delivery and pickup and time windows (VRPSDPTW), and experimental results show that the proposed method significantly improves the convergence performance and produces solutions with good distribution.

First Page

1914

Last Page

1928

CLC

U492.22; TP391

Recommended Citation

Zhang Wenqiang, Wang Xiaomeng, Zhang Xiaoxiao, et al. Hybrid Evolutionary Multi-objective Optimization Algorithm for Vehicle Routing Problem with Simultaneous Delivery and Pickup[J]. Journal of System Simulation, 2024, 36(8): 1914-1928.

DOI

10.16182/j.issn1004731x.joss.23-0834

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