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NGAP: a novel hybrid metaheuristic algorithm for round-trip carsharing fleet planning

Abstract : The growing awareness of the environmental movement greatly influences the transportation scene of this century leading to several transportation alternatives. One among them is carsharing service which has been gaining traction and support in major cities around the globe. It is also undeniable that the location planning of the fleet vehicles can contribute to its success. The fleet vehicles must be easily accessed and in the proximity of various transportation hubs and facilities. In this paper, we study the Vehicle Placement Problem (VPP) for round-trip carsharing and propose a novel hybrid algorithm, NGAP, which is a combination of NSGA-III and Pareto Local Search (PLS) to enhance the quality of the results over NSGA-III. The proposed algorithm is tested on 10 synthetic and four real-world instances. NGAP is shown to be significantly more efficient than NSGA-III on almost all instances in terms of Inverted Generational Distance (IGD), and Hypervolume.
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https://hal.archives-ouvertes.fr/hal-03352610
Contributor : Frédéric Guinand Connect in order to contact the contributor
Submitted on : Thursday, September 23, 2021 - 12:43:09 PM
Last modification on : Wednesday, November 3, 2021 - 4:17:17 AM

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Boonyarit Changaival, Grégoire Danoy, Dzmitry Kliazovich, Frédéric Guinand, Matthias Brust, et al.. NGAP: a novel hybrid metaheuristic algorithm for round-trip carsharing fleet planning. GECCO '20: Genetic and Evolutionary Computation Conference, Jul 2020, Cancún (on line), Mexico. pp.259-260, ⟨10.1145/3377929.3389941⟩. ⟨hal-03352610⟩

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