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DC Field | Value | Language |
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dc.citation.startPage | 103981 | - |
dc.citation.title | TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES | - |
dc.citation.volume | 148 | - |
dc.contributor.author | Bogyrbayeva, Aigerim | - |
dc.contributor.author | Yoon, Taehyun | - |
dc.contributor.author | Ko, Hanbum | - |
dc.contributor.author | Lim, Sungbin | - |
dc.contributor.author | Yun, Hyokun | - |
dc.contributor.author | Kwon, Changhyun | - |
dc.date.accessioned | 2023-12-21T12:45:12Z | - |
dc.date.available | 2023-12-21T12:45:12Z | - |
dc.date.created | 2023-07-03 | - |
dc.date.issued | 2023-03 | - |
dc.description.abstract | Reinforcement learning has recently shown promise in learning quality solutions in many combinatorial optimization problems. In particular, the attention-based encoder-decoder models show high effectiveness on various routing problems, including the Traveling Salesman Problem (TSP). Unfortunately, they perform poorly for the TSP with Drone (TSP-D), requiring routing a heterogeneous fleet of vehicles in coordination-a truck and a drone. In TSP-D, the two vehicles are moving in tandem and may need to wait at a node for the other vehicle to join. State-less attention-based decoder fails to make such coordination between vehicles. We propose a hybrid model that uses an attention encoder and a Long Short-Term Memory (LSTM) network decoder, in which the decoder's hidden state can represent the sequence of actions made. We empirically demonstrate that such a hybrid model improves upon a purely attention-based model for both solution quality and computational efficiency. Our experiments on the min-max Capacitated Vehicle Routing Problem (mmCVRP) also confirm that the hybrid model is more suitable for the coordinated routing of multiple vehicles than the attention-based model. The proposed model demonstrates comparable results as the operations research baseline methods. | - |
dc.identifier.bibliographicCitation | TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, v.148, pp.103981 | - |
dc.identifier.doi | 10.1016/j.trc.2022.103981 | - |
dc.identifier.issn | 0968-090X | - |
dc.identifier.scopusid | 2-s2.0-85146047173 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/64755 | - |
dc.identifier.wosid | 000992276800001 | - |
dc.language | 영어 | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.title | A deep reinforcement learning approach for solving the Traveling Salesman Problem with Drone | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.relation.journalWebOfScienceCategory | Transportation Science & Technology | - |
dc.relation.journalResearchArea | Transportation | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Vehicle routing | - |
dc.subject.keywordAuthor | Traveling salesman problem | - |
dc.subject.keywordAuthor | Drones | - |
dc.subject.keywordAuthor | Reinforcement learning | - |
dc.subject.keywordAuthor | Neural networks | - |
dc.subject.keywordPlus | NEIGHBORHOOD SEARCH | - |
dc.subject.keywordPlus | OPTIMIZATION | - |
dc.subject.keywordPlus | LOGISTICS | - |
dc.subject.keywordPlus | TRUCK | - |
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