Green Split Multiple-commodity Pickup and Delivery Vehicle Routing Problem

This paper studies a new version of the green split multiple-commodity pickup and delivery vehicle routing problem, in which the multiple commodity demands of each retail store could be split and each retail store could be visited multiple times. In addition, the reallocation of items collected from other retail stores, traffic congestion and time-windows for convenient pickup and delivery were considered when formulating the model. To solve this complex model, a two-stage search quantum particle swarm optimization (TSQPSO) algorithm is proposed. The two-stage search strategy of TSQPSO helps to find the optimization direction quickly. Meanwhile, the three designed neighbourhood structures help to prevent the algorithm from falling into local optimal solutions. The authors compared the results obtained by the GAMS optimization solver and their algorithm. The average gap between the results is 0.86%, but the authors' algorithm can obtain the optimal solution much faster. The computational results show that the model is capable of offering more reasonable and economical solutions. Furthermore, the authors found that with the lower unit operating commodity cost at the depot, the total cost is lower when the commodities are allowed to distribute from the depot. Conversely, when it is higher, the total cost is lower when the commodities collected from the supply retail stores are reallocated.

• Record URL:
  https://doi.org/10.1016/j.cor.2023.106318
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S030505482300182X
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/1793974
• Supplemental Notes:
  • © 2023 Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Zhao, Jiao
  • Dong, Hongxia
  • Wang, Ning
• Publication Date: 2023-11

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 106318
• Serial:
  • Computers & Operations Research
  • Volume: 159
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0305-0548
  • Serial URL: https://www.sciencedirect.com/journal/computers-and-operations-research

Subject/Index Terms

• TRT Terms: Optimization; Pickup and delivery service; Retail trade; Routing
• Identifier Terms: Vehicle Routing Problem
• Subject Areas: Freight Transportation; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01888441
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 21 2023 9:27AM