A new Hyper-heuristic based on Adaptive Simulated Annealing and Reinforcement Learning for the Capacitated Electric Vehicle Routing Problem

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dc.contributor.authorRodríguez Esparza, Erick
dc.contributor.authorMasegosa Arredondo, Antonio David
dc.contributor.authorOliva, Diego
dc.contributor.authorOnieva Caracuel, Enrique
dc.date.accessioned2025-03-25T13:48:27Z
dc.date.available2025-03-25T13:48:27Z
dc.date.issued2024-10-15
dc.date.updated2025-03-25T13:48:27Z
dc.description.abstractElectric vehicles (EVs) have been adopted in urban areas to reduce environmental pollution and global warming due to the increasing number of freight vehicles. However, there are still deficiencies in routing the trajectories of last-mile logistics that continue to impact social and economic sustainability. For that reason, in this paper, a hyper-heuristic (HH) approach called Hyper-heuristic Adaptive Simulated Annealing with Reinforcement Learning (HHASARL) is proposed. It is composed of a multi-armed bandit method and the self-adaptive Simulated Annealing (SA) metaheuristic algorithm for solving the problem called Capacitated Electric Vehicle Routing Problem (CEVRP). Due to the limited number of charging stations and the travel range of EVs, the EVs must require battery recharging moments in advance and reduce travel times and costs. The implementation of the HH improves multiple minimum best-known solutions and obtains the best mean values for some high-dimensional instances for the proposed benchmark for the IEEE WCCI2020 competition.en
dc.description.sponsorshipThis research has been partially funded by the University of Deusto Research Training Grants Programme, by the Spanish Ministry of Science and Innovation through the research project PID2022-140612OBI00 and by the Basque Government through the research grants IT1564-22, KK-2023/00012 and KK-2023/00038. This research has also been partially supported by European Union’s Horizon 2020 research and innovation programme under grant agreement No. 861540 [project SENATOR (Smart Network Operator Platform enabling Shared, Integrated and more Sustainable Urban Freight Logistics)]en
dc.identifier.citationRodríguez-Esparza, E., Masegosa, A. D., Oliva, D., & Onieva, E. (2024). A new Hyper-heuristic based on Adaptive Simulated Annealing and Reinforcement Learning for the Capacitated Electric Vehicle Routing Problem. Expert Systems with Applications, 252. https://doi.org/10.1016/J.ESWA.2024.124197
dc.identifier.doi10.1016/J.ESWA.2024.124197
dc.identifier.issn0957-4174
dc.identifier.urihttp://hdl.handle.net/20.500.14454/2565
dc.language.isoeng
dc.publisherElsevier Ltd
dc.rights© 2024 The Author(s)
dc.subject.otherCapacitated electric vehicle routing problem
dc.subject.otherCombinatorial optimization
dc.subject.otherElectric vehicles
dc.subject.otherHyper-heuristic
dc.subject.otherLast-mile logistics
dc.subject.otherReinforcement learning
dc.titleA new Hyper-heuristic based on Adaptive Simulated Annealing and Reinforcement Learning for the Capacitated Electric Vehicle Routing Problemen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.titleExpert Systems with Applications
oaire.citation.volume252
oaire.licenseConditionhttps://creativecommons.org/licenses/by-nc-nd/4.0/
oaire.versionVoR
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