A bi-criteria evolutionary algorithm for a constrained multi-depot vehicle routing problem
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500_2016_Article_2112.pdf
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Author(s)
Agrawal, Vikas
Lightner, Constance
Lightner-Laws, Carin
Wagner, Neal
Date Issued
April 2016
Journal
Soft Computing
Publisher
Springer Berlin Heidelberg
Citation
Agrawal, Vikas, Constance Lightner, Carin Lightner-Laws, and Neal Wagner. “A Bi-Criteria Evolutionary Algorithm for a Constrained Multi-Depot Vehicle Routing Problem.” Soft Computing (April 8, 2016).
Version
Author's final manuscript
Abstract
Most research about the vehicle routing problem (VRP) does not collectively address many of the constraints that real-world transportation companies have regarding route assignments. Consequently, our primary objective is to explore solutions for real-world VRPs with a heterogeneous fleet of vehicles, multi-depot subcontractors (drivers), and pickup/delivery time window and location constraints. We use a nested bi-criteria genetic algorithm (GA) to minimize the total time to complete all jobs with the fewest number of route drivers. Our model will explore the issue of weighting the objectives (total time vs. number of drivers) and provide Pareto front solutions that can be used to make decisions on a case-by-case basis. Three different real-world data sets were used to compare the results of our GA vs. transportation field experts’ job assignments. For the three data sets, all 21 Pareto efficient solutions yielded improved overall job completion times. In 57 % (12/21) of the cases, the Pareto efficient solutions also utilized fewer drivers than the field experts’ job allocation strategies.
MIT Department
Lincoln Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1007/s00500-016-2112-3
