Collaborative Delivery Optimization With Multiple Drones via Constrained Hybrid Pointer Network

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dc.contributor.authorKong, Fanhui
dc.contributor.authorJiang, Bin
dc.contributor.authorWang, Jian
dc.contributor.authorWang, Huihui
dc.contributor.authorSong, Houbing
dc.date.accessioned2023-10-17T18:22:09Z
dc.date.available2023-10-17T18:22:09Z
dc.date.issued2023-09-25
dc.description.abstractDrone participation in truck delivery is a potential booster for the last-mile logistics system, which has been an emerging hot research field. Among that, how to arrange a fleet of drones from the truck and optimize the vehicle routing problem with drones (VRPD) is a key issue. However, most existing studies fail to derive the feasible solutions due to unordered customer distributions and multi-variant drone feature constraints. In this paper, we propose a novel self-driven reinforcement learning structure, named constraint-based hybrid pointer network(CH-Ptr-Net) model, which is a hybrid pointer network approach composed of graph neural network(GNN) embedding and attention decoder. We go into developing the simpler embedding version for multiple drones-assisted truck delivery. The CH-Ptr-Net model tends to generate a set of optimal delivery sequence, after constructing the mixed integer linear program(MILP) formulation. Extensive numerical testing indicates that the proposed method performs better than recent exact and heuristic approaches for collaborative delivery routing optimization with the truck carrying multiple drones.en_US
dc.description.sponsorshipThis work was supported in part by Humanities and Social Sciences Research Project of the Ministry of Education of China under Grant 21YJC630051, National Natural Science Foundation of China under Grant 62102264, Youth Foundation of Shandong Natural Science Foundation under Grant ZR2023QG034, Youth Innovation University Team Project in Shandong under Grant 2022KJ062, Independent Innovation Fund of China University of Petroleum (East China) under Grant 22CX06056A and Talent Project of Qingdao University of Technology under Grant 901020220046.en_US
dc.description.urihttps://ieeexplore.ieee.org/abstract/document/10261492en_US
dc.format.extent16 pagesen_US
dc.genrejournal articlesen_US
dc.genrepostprintsen_US
dc.identifierdoi:10.13016/m2l95z-xb8w
dc.identifier.citationKong, Fanhui, Bin Jiang, Jian Wang, Huihui Wang, and Houbing Song. “Collaborative Delivery Optimization With Multiple Drones via Constrained Hybrid Pointer Network.” IEEE Internet of Things Journal, 2023, 1–1. https://doi.org/10.1109/JIOT.2023.3318524.en_US
dc.identifier.urihttps://doi.org/10.1109/JIOT.2023.3318524
dc.identifier.urihttp://hdl.handle.net/11603/30235
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rights© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.titleCollaborative Delivery Optimization With Multiple Drones via Constrained Hybrid Pointer Networken_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0003-2631-9223en_US

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