A genetic algorithm for a green vehicle routing problem

Paulo De Oliveira Da Costa; Stefano Mauceri; Paula  Carroll; Fabiano Pallonetto

doi:10.1016/j.endm.2018.01.008

A genetic algorithm for a green vehicle routing problem

Paulo De Oliveira Da Costa, Stefano Mauceri, Paula Carroll, Fabiano Pallonetto

Research output: Contribution to journal › Article › Academic › peer-review

83 Citations (Scopus)

Abstract

We propose a Genetic Algorithm (GA) to address a Green Vehicle Routing Problem (G-VRP). Unlike classic formulations of the VRP, this study aims to minimise the CO2 emissions per route. The G-VRP is of interest to policy makers who wish to reduce greenhouse gas emissions. The GA is tested on a suite of benchmark, and real-world instances which include road speed and gradient data. Our solution approach incorporates elements of local and population search heuristics. Solutions are compared with routes currently used by drivers in a courier company. Reductions in emissions are achieved without incurring additional operational costs.

Original language	English
Pages (from-to)	65-74
Number of pages	10
Journal	Electronic Notes in Discrete Mathematics
Volume	64
DOIs	https://doi.org/10.1016/j.endm.2018.01.008
Publication status	Published - 1 Feb 2018
Externally published	Yes
Event	8th international network optimization conference, (INOC2017) - Lisboa, Portugal Duration: 26 Feb 2017 → 28 Feb 2017 http://inoc2017.fc.ul.pt/

Bibliographical note

Part of special issue:
8th International Network Optimization Conference - INOC 2017

Keywords

Green Vehicle Routing Problem
Genetic Algorithms
Genetic Algorithm

Access to Document

10.1016/j.endm.2018.01.008

Cite this

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title = "A genetic algorithm for a green vehicle routing problem",

abstract = "We propose a Genetic Algorithm (GA) to address a Green Vehicle Routing Problem (G-VRP). Unlike classic formulations of the VRP, this study aims to minimise the CO2 emissions per route. The G-VRP is of interest to policy makers who wish to reduce greenhouse gas emissions. The GA is tested on a suite of benchmark, and real-world instances which include road speed and gradient data. Our solution approach incorporates elements of local and population search heuristics. Solutions are compared with routes currently used by drivers in a courier company. Reductions in emissions are achieved without incurring additional operational costs.",

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T1 - A genetic algorithm for a green vehicle routing problem

AU - De Oliveira Da Costa, Paulo

AU - Mauceri, Stefano

AU - Carroll, Paula

AU - Pallonetto, Fabiano

N1 - Part of special issue: 8th International Network Optimization Conference - INOC 2017

PY - 2018/2/1

Y1 - 2018/2/1

N2 - We propose a Genetic Algorithm (GA) to address a Green Vehicle Routing Problem (G-VRP). Unlike classic formulations of the VRP, this study aims to minimise the CO2 emissions per route. The G-VRP is of interest to policy makers who wish to reduce greenhouse gas emissions. The GA is tested on a suite of benchmark, and real-world instances which include road speed and gradient data. Our solution approach incorporates elements of local and population search heuristics. Solutions are compared with routes currently used by drivers in a courier company. Reductions in emissions are achieved without incurring additional operational costs.

AB - We propose a Genetic Algorithm (GA) to address a Green Vehicle Routing Problem (G-VRP). Unlike classic formulations of the VRP, this study aims to minimise the CO2 emissions per route. The G-VRP is of interest to policy makers who wish to reduce greenhouse gas emissions. The GA is tested on a suite of benchmark, and real-world instances which include road speed and gradient data. Our solution approach incorporates elements of local and population search heuristics. Solutions are compared with routes currently used by drivers in a courier company. Reductions in emissions are achieved without incurring additional operational costs.

KW - Green Vehicle Routing Problem

KW - Genetic Algorithms

KW - Genetic Algorithm

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M3 - Article

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EP - 74

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JF - Electronic Notes in Discrete Mathematics

T2 - 8th international network optimization conference, (INOC2017)

Y2 - 26 February 2017 through 28 February 2017

ER -

A genetic algorithm for a green vehicle routing problem

Abstract

Bibliographical note

Keywords

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