A hybrid evolutionary algorithm for heterogeneous fleet vehicle routing problems with time windows

Çaʇri Koç, Tolga Bektaş, Ola Jabali, Gilbert Laporte

Research output: Contribution to journalArticle

66 Citations (Scopus)

Abstract

This paper presents a hybrid evolutionary algorithm (HEA) to solve heterogeneous fleet vehicle routing problems with time windows. There are two main types of such problems, namely the fleet size and mix vehicle routing problem with time windows (F) and the heterogeneous fixed fleet vehicle routing problem with time windows (H), where the latter, in contrast to the former, assumes a limited availability of vehicles. The main objective is to minimize the fixed vehicle cost and the distribution cost, where the latter can be defined with respect to en-route time (T) or distance (D). The proposed unified algorithm is able to solve the four variants of heterogeneous fleet routing problem, called FT, FD, HT and HD, where the last variant is new. The HEA successfully combines several metaheuristics and offers a number of new advanced efficient procedures tailored to handle the heterogeneous fleet dimension. Extensive computational experiments on benchmark instances have shown that the HEA is highly effective on FT, FD and HT. In particular, out of the 360 instances we obtained 75 new best solutions and matched 102 within reasonable computational times. New benchmark results on HD are also presented.

Original languageEnglish
Pages (from-to)11-27
Number of pages17
JournalComputers and Operations Research
Volume64
DOIs
Publication statusPublished - 8 Jun 2015

Keywords

  • Genetic algorithm
  • Heterogeneous fleet
  • Neighborhood search
  • Time windows
  • Vehicle routing

ASJC Scopus subject areas

  • Computer Science(all)
  • Modelling and Simulation
  • Management Science and Operations Research

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