Improved Ant Colony Algorithm for Split Delivery Vehicle Routing Problem with Capacity Constraint

Shasha Zeng () and Jianqin Zhou ()
Additional contact information
Shasha Zeng: Beijing Jiaotong University
Jianqin Zhou: Beijing Jiaotong University

A chapter in LISS 2023, 2024, pp 393-405 from Springer

Abstract: Abstract The traditional vehicle routing problem (VRP) is a typical NP-hard problem in combinatorial optimization, based on the premise that customer demands are indivisible. However, in practical logistics operations, sometimes splitting demands can lead to better cost reduction in transportation. In this paper, we establish an integer programming model for split delivery vehicle routing problem (SDVRP) with capacity constraint and design an improved Ant Colony Algorithm tailored to the characteristics of the model, with the main design idea being the innovation of a mechanism for selecting splitting points. Through computational experiments, we compare the solution results with the traditional VRP, demonstrating the superiority of demand splitting. Additionally, we compare the results with those obtained in other studies using the same instances, confirming that the algorithm proposed in this paper has certain advantages in solving the split delivery vehicle routing problem.

Keywords: Demand Splitting; Vehicle Routing Problem; Improved Ant Colony Algorithm (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:spr:lnopch:978-981-97-4045-1_31

Ordering information: This item can be ordered from
http://www.springer.com/9789819740451

DOI: 10.1007/978-981-97-4045-1_31

Access Statistics for this chapter

More chapters in Lecture Notes in Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().