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

Aston Medical Research Institute, Aston University Medical School, Aston University, Birmingham B15 2TH, UK

Research output: Chapter in Book/Published conference output › Conference publication

5 Citations (Scopus)

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

Access to Document

10.1145/3319619.3322048

Cite this

@inproceedings{28164ba5498b4516ab9d7564f092a33d,

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",

author = "Chitty, {Darren M.} and Elizabeth Wanner and Rakhi Parmar and Lewis, {Peter R.}",

year = "2019",

month = jul,

day = "13",

doi = "10.1145/3319619.3322048",

language = "English",

isbn = "978-1-4503-6748-6",

pages = "97--98",

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

publisher = "ACM",

address = "United States",

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

}

TY - GEN

T1 - Scaling ACO to large-scale vehicle fleet optimisation via partial-ACO

AU - Chitty, Darren M.

AU - Wanner, Elizabeth

AU - Parmar, Rakhi

AU - Lewis, Peter R.

PY - 2019/7/13

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

UR - http://www.scopus.com/inward/record.url?scp=85070639341&partnerID=8YFLogxK

U2 - 10.1145/3319619.3322048

DO - 10.1145/3319619.3322048

M3 - Conference publication

SN - 978-1-4503-6748-6

SP - 97

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

Access to Document

Other files and links

Fingerprint

Cite this