Particle Swarm Optimization for Split Delivery Vehicle Routing Problem

Jianli Shi, Jin Zhang, Kun Wang and Xin Fang
Additional contact information
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

Asia-Pacific Journal of Operational Research (APJOR), 2018, vol. 35, issue 02, 1-42

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%.

Keywords: Split delivery; VRP; particle swarm optimization (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0217595918400067
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:apjorx:v:35:y:2018:i:02:n:s0217595918400067

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0217595918400067

Access Statistics for this article

Asia-Pacific Journal of Operational Research (APJOR) is currently edited by Gongyun Zhao

More articles in Asia-Pacific Journal of Operational Research (APJOR) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().