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Incorporating emissions models within a multi-objectivevehicle routing problem

Published: 06 July 2013 Publication History

Abstract

The vehicle routing problem with time windows (VRPTW) has previously been investigated as a multi-objective problem. In this paper estimated carbon emissions is added as an objective alongside the number of vehicles required and distance travelled. We term this new problem formulation (E)VRPTW. In order to estimate emissions we require detailed information regarding the nature of the route to be taken. As previous benchmark VRPTW problem instances do not supply such information we generate new problem instances based upon street network data from Open Street Map. Results suggest that by adding emissions as the third objective, in many cases the search may be directed to areas that allow improvement in the distance and vehicles objectives. As emissions and distance are inherently related, we do not search for pareto fronts. Rather we attempt to find solutions that either minimise distance or minimise vehicles used. Adding the third emissions objective is shown to enable a multi-objective EA to find improved solutions in terms of minimal vehicles or minimal distance when compared to the same multi-objective EA using only two objectives.

References

[1]
COPERT-4, http://www.emisia.com/copert/, 2010.
[2]
Kalyanmoy Deb, Samir Agrawal, Amrit Pratap, and T. Meyarivan, A fast and elitist multiobjective genetic algorithm: Nsga-ii, IEEE Trans. Evolutionary Computation 6 (2002), no. 2, 182--197.
[3]
Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and T. Meyarivan, A fast elitist multi-objective genetic algorithm: Nsga-ii, IEEE Transactions on Evolutionary Computation 6 (2000), 182--197.
[4]
Beatrice Ombuki, Brian J. Ross, and Franklin Hanshar, Multi-objective genetic algorithms for vehicle routing problem with time windows, Applied Intelligence 24 (2006), 17--30.
[5]
Beatrice Ombuki, Brian J. Ross, and Franklin Hanshar, Multi-objective genetic algorithms for vehicle routing problem with time windows, Applied Intelligence 24 (2006), 17--30.

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  1. Incorporating emissions models within a multi-objectivevehicle routing problem

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    cover image ACM Conferences
    GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
    July 2013
    1798 pages
    ISBN:9781450319645
    DOI:10.1145/2464576
    • Editor:
    • Christian Blum,
    • General Chair:
    • Enrique Alba
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 06 July 2013

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    Author Tags

    1. low co2 routing
    2. multi-objective optimisation
    3. vehicle routing

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    GECCO '13
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    GECCO '13: Genetic and Evolutionary Computation Conference
    July 6 - 10, 2013
    Amsterdam, The Netherlands

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    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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