Hybrid adaptive large neighborhood search algorithm for the mixed fleet heterogeneous dial-a-ride problem

Masmoudi, Mohamed Amine, Hosny, Manar, Demir, Emrah ORCID: https://orcid.org/0000-0002-4726-2556 and Pesch, Erwin 2020. Hybrid adaptive large neighborhood search algorithm for the mixed fleet heterogeneous dial-a-ride problem. Journal of Heuristics 26 (1) , pp. 83-118. 10.1007/s10732-019-09424-x

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Abstract

The mixed fleet heterogeneous dial-a-ride problem (MF-HDARP) consists of designing vehicle routes for a set of users by using a mixed fleet including both heterogeneous conventional and alternative fuel vehicles. In addition, a vehicle is allowed to refuel from a fuel station to eliminate the risk of running out of fuel during its service. We propose an efficient hybrid adaptive large neighborhood search (hybrid ALNS) algorithm for the MF-HDARP. The computational experiments show that the algorithm produces high quality solutions on our generated instances and on HDARP benchmarks instances. Computational experiments also highlight that the newest components added to the standard ALNS algorithm enhance intensification and diversification during the search process.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Business (Including Economics)
Publisher:	Springer Verlag (Germany)
ISSN:	1381-1231
Date of First Compliant Deposit:	3 September 2019
Date of Acceptance:	28 August 2019
Last Modified:	27 Nov 2024 05:15
URI:	https://orca.cardiff.ac.uk/id/eprint/125255

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