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

Darren M. Chitty; Elizabeth Wanner; Rakhi Parmar; Peter R. Lewis

doi:10.1145/3319619.3322048

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

Darren M. Chitty, Elizabeth Wanner, Rakhi Parmar, Peter R. Lewis

Research output: Chapter in Book/Published conference output › Conference 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 language	English
Title of host publication	GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
Publisher	ACM
Pages	97-98
Number of pages	2
ISBN (Electronic)	9781450367486
ISBN (Print)	978-1-4503-6748-6
DOIs	https://doi.org/10.1145/3319619.3322048
Publication status	Published - 13 Jul 2019
Event	the Genetic and Evolutionary Computation Conference Companion - Prague, Czech Republic Duration: 13 Jul 2019 → 17 Jul 2019

Conference

Conference	the Genetic and Evolutionary Computation Conference Companion
Period	13/07/19 → 17/07/19

Keywords

ACO
Fleet Optimisation
Multi-Depot Vehicle Routing Problem

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10.1145/3319619.3322048

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title = "Scaling ACO to large-scale vehicle fleet optimisation via partial-ACO",

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%.",

keywords = "ACO, Fleet Optimisation, Multi-Depot Vehicle Routing Problem",

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booktitle = "GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion",

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note = "the Genetic and Evolutionary Computation Conference Companion ; Conference date: 13-07-2019 Through 17-07-2019",

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AU - Chitty, Darren M.

AU - Wanner, Elizabeth

AU - Parmar, Rakhi

AU - Lewis, Peter R.

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Y1 - 2019/7/13

N2 - 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%.

AB - 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%.

KW - ACO

KW - Fleet Optimisation

KW - Multi-Depot Vehicle Routing Problem

UR - http://dl.acm.org/citation.cfm?doid=3319619.3322048

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M3 - Conference publication

SN - 978-1-4503-6748-6

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EP - 98

BT - GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion

PB - ACM

T2 - the Genetic and Evolutionary Computation Conference Companion

Y2 - 13 July 2019 through 17 July 2019

ER -

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

Abstract

Conference

Keywords

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