Solving the Feeder Vehicle Routing Problem using ant colony optimization

Ying Hua Huang, Carola A. Blazquez, Shan Huen Huang, Germán Paredes-Belmar, Guillermo Latorre-Nuñez

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)

Abstract

This paper studies the Feeder Vehicle Routing Problem (FVRP), a new variant of the vehicle routing problem (VRP), in which each customer is served by either a large (truck) or a small vehicle (motorcycle). In this particular type of delivery, the trucks and the motorcycles must depart from the depot, visit the customers, and eventually return to the depot. During the delivery process, the motorcycles travel to the truck locations for reloading. The ant colony optimization (ACO) algorithm is employed for solving the problem with the objective of determining the number of dispatching sub-fleets and optimal routes to minimize the total cost (fixed route and travel costs). Three benchmark datasets are generated to examine the performance of the FVPR. For comparison purposes, all instances are executed by dispatching only trucks as in the traditional VRP and a four-stage hierarchical heuristic. Additionally, ACO is compared to optimal solutions for small instances. The results indicate that the proposed ACO algorithm yields promising solutions particularly for large instances within a reasonable time frame in an efficient manner.

Original languageEnglish
Pages (from-to)520-535
Number of pages16
JournalComputers and Industrial Engineering
Volume127
DOIs
Publication statusAccepted/In press - 1 Jan 2018

Keywords

  • Ant colony optimization
  • Heterogeneous vehicles
  • Metaheuristics
  • Vehicle routing problem

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)

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