Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/126987
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Type: Conference paper
Title: Dynamic bi-objective routing of multiple vehicles
Author: Bossek, J.
Grimme, C.
Trautmann, H.
Citation: Proceedings of the 2020 Genetic and Evolutionary Computation Conference (GECCO'20), 2020 / vol.abs/2005.13872, pp.166-174
Publisher: Association for Computing Machinery
Publisher Place: New York
Issue Date: 2020
ISBN: 9781450371285
Conference Name: Genetic and Evolutionary Computation Conference (GECCO) (08 Jul 2020 - 12 Jul 2020 : Cancún Mexico)
Statement of
Responsibility: 
Jakob Bossek, Christian Grimme, Heike Trautmann
Abstract: In practice, e.g. in delivery and service scenarios, Vehicle-Routing- Problems (VRPs) often imply repeated decision making on dynamic customer requests. As in classical VRPs, tours have to be planned short while the number of serviced customers has to be maximized at the same time resulting in a multi-objective problem. Beyond that, however, dynamic requests lead to the need for re-planning of not yet realized tour parts, while already realized tour parts are irreversible. In this paper we study this type of bi-objective dynamic VRP including sequential decision making and concurrent realization of decisions. We adopt a recently proposed Dynamic Evolutionary Multi-Objective Algorithm (DEMOA) for a related VRP problem and extend it to the more realistic (here considered) scenario of multiple vehicles.We empirically showthat our DEMOA is competitive with a multi-vehicle offline and clairvoyant variant of the proposed DEMOA as well as with the dynamic single-vehicle approach proposed earlier.
Keywords: Vehicle routing; decision making; multi-objective optimization; dynamic optimization; evolutionary algorithms
Rights: © 2020 Copyright held by the owner/author(s). Publication rights licensed to ACM
RMID: 1000022133
DOI: 10.1145/3377930.3390146
Published version: https://dl.acm.org/doi/proceedings/10.1145/3377930
Appears in Collections:Computer Science publications

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