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Particle Swarm Optimization for Split Delivery Vehicle Routing Problem

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  • Jianli Shi

    (School of Transportation and Logistics, Southwest Jiaotong University, 610031, Chengdu, Sichuan Province, P. R. China2National United Engineering Laboratory, of Integrated and Intelligent Transportation, Southwest Jiaotong University, 610031, Chengdu, Sichuan Province, P. R. China)

  • Jin Zhang

    (School of Transportation and Logistics, Southwest Jiaotong University, 610031, Chengdu, Sichuan Province, P. R. China2National United Engineering Laboratory, of Integrated and Intelligent Transportation, Southwest Jiaotong University, 610031, Chengdu, Sichuan Province, P. R. China)

  • Kun Wang

    (School of Transportation and Logistics, Southwest Jiaotong University, 610031, Chengdu, Sichuan Province, P. R. China)

  • Xin Fang

    (Chongqing Engineering Research Center for Processing, Storage and Transportation of Characterized, Agro-Products, Chongqing 400067, P. R. China4School of Business Planning, Chongqing Technology and Business University, Chongqing 400067, P. R. China)

Abstract

The split delivery vehicle routing problem (SDVRP) is a variation of the capacitated vehicle routing problem in which some customers may be served by more than one vehicle. We have proposed a particle swarm optimization approach that incorporates a local search to solve the SDVRP. An integer coding method was presented, and a decoding method based on Bellman’s equation was modified for the SDVRP. A way to address the differences in the length of the velocity vector, the position vector, the personal best position vector, the local best position vector and the global best position vector was designed. Two groups of local searches for top solutions were incorporated into the algorithm, with the ability to control whether they are executed on a given solution. The algorithm was initially tested using the modified Solomon’s instances to verify the parameters used, including the local search probability, the size of the swarm, the velocity equation and the length of the vectors. Extensive computational experiments were carried out on 131 benchmark instances available in the literature. The results obtained were competitive. More precisely, equally good solutions were found in 32 instances, and improved solutions were found in 35 instances, with an average improvement of 0.02% and a maximum improvement of 1.12%.

Suggested Citation

  • Jianli Shi & Jin Zhang & Kun Wang & Xin Fang, 2018. "Particle Swarm Optimization for Split Delivery Vehicle Routing Problem," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 35(02), pages 1-42, April.
    • Handle: RePEc:wsi:apjorx:v:35:y:2018:i:02:n:s0217595918400067
    DOI: 10.1142/S0217595918400067
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    References listed on IDEAS

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    1. C. Archetti & M. Bouchard & G. Desaulniers, 2011. "Enhanced Branch and Price and Cut for Vehicle Routing with Split Deliveries and Time Windows," Transportation Science, INFORMS, vol. 45(3), pages 285-298, August.
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    Cited by:

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    3. Samuel Reong & Hui-Ming Wee & Yu-Lin Hsiao, 2022. "20 Years of Particle Swarm Optimization Strategies for the Vehicle Routing Problem: A Bibliometric Analysis," Mathematics, MDPI, vol. 10(19), pages 1-19, October.

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