Scaling ACO to large-scale vehicle fleet optimisation via partial-ACO

Research output: Chapter in Book/Report/Conference proceedingConference publication

Abstract

The benefits of optimising fleets of vehicles regards scheduling tasks are threefold; reduced costs, reduced road use, and most importantly, reduced emissions. However, optimisation methods, both exact and meta-heuristic, scale poorly. This issue is addressed with Partial-ACO, a novel variant of ACO that scales by ants only partially modifying good solutions. For real-world fleet optimisation problems supplied by a Birmingham company of up to 298 jobs and 32 vehicles, Partial-ACO demonstrates better scalability than ACO and GAs reducing the company's fleet traversal by over 40%.
Original languageEnglish
Title of host publicationGECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
PublisherACM
Pages97-98
Number of pages2
ISBN (Electronic)9781450367486
ISBN (Print)978-1-4503-6748-6
DOIs
Publication statusPublished - 13 Jul 2019
Eventthe Genetic and Evolutionary Computation Conference Companion - Prague, Czech Republic
Duration: 13 Jul 201917 Jul 2019

Conference

Conferencethe Genetic and Evolutionary Computation Conference Companion
Period13/07/1917/07/19

Keywords

  • ACO
  • Fleet Optimisation
  • Multi-Depot Vehicle Routing Problem

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  • Cite this

    Chitty, D. M., Wanner, E., Parmar, R., & Lewis, P. R. (2019). Scaling ACO to large-scale vehicle fleet optimisation via partial-ACO. In GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion (pp. 97-98). ACM. https://doi.org/10.1145/3319619.3322048