Minimum Cost Flow-Based Integrated Model for Electric Vehicle and Crew Scheduling
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| Publicado en: | Journal of Advanced Transportation vol. 2023 (2023) |
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| Autor principal: | |
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| Publicado: |
John Wiley & Sons, Inc.
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 024 | 7 | |a 10.1155/2023/6658030 |2 doi | |
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| 045 | 2 | |b d20230101 |b d20231231 | |
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| 100 | 1 | |a Shen, Yindong |u School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China; Key Laboratory of Image Processing and Intelligent Control, Huazhong University of Science and Technology, Ministry of Education, Wuhan, China | |
| 245 | 1 | |a Minimum Cost Flow-Based Integrated Model for Electric Vehicle and Crew Scheduling | |
| 260 | |b John Wiley & Sons, Inc. |c 2023 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Vehicle and crew scheduling is vital in public transit planning. Conventionally, the issues are handled sequentially as the vehicle scheduling problem (VSP) and crew scheduling problem (CSP). However, integrating these planning steps offers additional flexibility, resulting in improved efficiency compared with sequential planning. Given the ever-growing market share of electric buses, this paper introduces a new model for integrated electric VSP and CSP, called EVCSPM. This model employs the minimum cost flow formulations for electric VSP, set partitioning for CSP, and linking constraints. Due to the nonlinear integer property of EVCSPM, we propose a method that hybrids a matching-based heuristic and integer linear programming solver, GUROBI. The numerical results demonstrate the efficiency of our methodology, and the integrated model outperforms the sequential model in real-life scenarios. | |
| 653 | |a Schedules | ||
| 653 | |a Scheduling | ||
| 653 | |a Electric vehicles | ||
| 653 | |a Linear programming | ||
| 653 | |a Public transportation | ||
| 653 | |a Hybrids | ||
| 653 | |a Integer programming | ||
| 653 | |a Decomposition | ||
| 653 | |a Transportation planning | ||
| 653 | |a Algorithms | ||
| 653 | |a Heuristic | ||
| 653 | |a Energy consumption | ||
| 653 | |a Minimum cost | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Li, Yuanyuan |u School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China; Key Laboratory of Image Processing and Intelligent Control, Huazhong University of Science and Technology, Ministry of Education, Wuhan, China | |
| 773 | 0 | |t Journal of Advanced Transportation |g vol. 2023 (2023) | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2889074859/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/2889074859/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2889074859/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |