A MultiObjective Optimization Approach for Integrated Timetabling and Vehicle Scheduling with Uncertainty
The timetabling problem (TTP) and vehicle scheduling problem (VSP) are two indispensable problems in public transit planning process. They used to be solved in sequence; hence, optimality of resulting solutions is compromised. To get better results, some integrated approaches emerge to solve the TTP...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/3529984 |
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| _version_ | 1832550902513270784 |
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| author | Yindong Shen Wenliang Xie Jingpeng Li |
| author_facet | Yindong Shen Wenliang Xie Jingpeng Li |
| author_sort | Yindong Shen |
| collection | DOAJ |
| description | The timetabling problem (TTP) and vehicle scheduling problem (VSP) are two indispensable problems in public transit planning process. They used to be solved in sequence; hence, optimality of resulting solutions is compromised. To get better results, some integrated approaches emerge to solve the TTP and VSP as an integrated problem. In the existing integrated approaches, the passenger comfort on bus and the uncertainty in the real world are rarely considered. To provide better service for passengers and enhance the robustness of the schedule to be compiled, we study the integrated optimization of TTP and VSP with uncertainty. In this paper, a novel multiobjective optimization approach with the objectives of minimizing the passenger travel cost, the vehicle scheduling cost, and the incompatible trip-link cost is proposed. Meanwhile, a multiobjective hybrid algorithm, which is a combination of the self-adjust genetic algorithm (SGA), large neighborhood search (LNS) algorithm, and Pareto separation operator (PSO), is applied to solve the integrated optimization problem. The experimental results show that the approach outperforms existing approaches in terms of service level and robustness. |
| format | Article |
| id | doaj-art-52ed8f77e60f4224b20c9d5c8bd4130b |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-52ed8f77e60f4224b20c9d5c8bd4130b2025-02-03T06:05:32ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/35299843529984A MultiObjective Optimization Approach for Integrated Timetabling and Vehicle Scheduling with UncertaintyYindong Shen0Wenliang Xie1Jingpeng Li2Key Laboratory of Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, ChinaKey Laboratory of Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, ChinaDivision of Computing Science and Mathematics, University of Stirling, Stirling, UKThe timetabling problem (TTP) and vehicle scheduling problem (VSP) are two indispensable problems in public transit planning process. They used to be solved in sequence; hence, optimality of resulting solutions is compromised. To get better results, some integrated approaches emerge to solve the TTP and VSP as an integrated problem. In the existing integrated approaches, the passenger comfort on bus and the uncertainty in the real world are rarely considered. To provide better service for passengers and enhance the robustness of the schedule to be compiled, we study the integrated optimization of TTP and VSP with uncertainty. In this paper, a novel multiobjective optimization approach with the objectives of minimizing the passenger travel cost, the vehicle scheduling cost, and the incompatible trip-link cost is proposed. Meanwhile, a multiobjective hybrid algorithm, which is a combination of the self-adjust genetic algorithm (SGA), large neighborhood search (LNS) algorithm, and Pareto separation operator (PSO), is applied to solve the integrated optimization problem. The experimental results show that the approach outperforms existing approaches in terms of service level and robustness.http://dx.doi.org/10.1155/2021/3529984 |
| spellingShingle | Yindong Shen Wenliang Xie Jingpeng Li A MultiObjective Optimization Approach for Integrated Timetabling and Vehicle Scheduling with Uncertainty Journal of Advanced Transportation |
| title | A MultiObjective Optimization Approach for Integrated Timetabling and Vehicle Scheduling with Uncertainty |
| title_full | A MultiObjective Optimization Approach for Integrated Timetabling and Vehicle Scheduling with Uncertainty |
| title_fullStr | A MultiObjective Optimization Approach for Integrated Timetabling and Vehicle Scheduling with Uncertainty |
| title_full_unstemmed | A MultiObjective Optimization Approach for Integrated Timetabling and Vehicle Scheduling with Uncertainty |
| title_short | A MultiObjective Optimization Approach for Integrated Timetabling and Vehicle Scheduling with Uncertainty |
| title_sort | multiobjective optimization approach for integrated timetabling and vehicle scheduling with uncertainty |
| url | http://dx.doi.org/10.1155/2021/3529984 |
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