A Dual-Population Constrained Multi-Objective Evolutionary Algorithm with Success Incentive Mechanism and its application to uncertain multimodal transportation problems
The evolution of the transportation industry has heightened the focus on environmentally sustainable multimodal transport, particularly in addressing carbon emissions. In modern logistics, path planning under uncertainty has become a pivotal research area. This paper proposes a multi-objective, mult...
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| Vydáno v: | Engineering applications of artificial intelligence Ročník 162; s. 112586 |
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| Jazyk: | angličtina |
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Elsevier Ltd
26.12.2025
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| ISSN: | 0952-1976 |
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| Abstract | The evolution of the transportation industry has heightened the focus on environmentally sustainable multimodal transport, particularly in addressing carbon emissions. In modern logistics, path planning under uncertainty has become a pivotal research area. This paper proposes a multi-objective, multi-constraint optimization model for multimodal transport that aims to concurrently minimize cost, carbon emissions, and time. The model accounts for numerous operational constraints, including timetables, as well as dual sources of uncertainty from demand and the transport environment. To solve this complex problem, this paper introduces a new algorithmic framework. The proposed algorithm, a Dual-Population Constrained Multi-Objective Evolutionary Algorithm with a Success Incentive Mechanism (DSCMOEA), integrates three key innovations: a universal priority-based encoding/decoding adapter, a specialized constraint-handling architecture, and an adaptive operator selection mechanism. The adapter is central to the framework, enabling continuous-domain evolutionary algorithms to solve the discrete transport problem without internal modification. This approach also provides the versatility to handle various uncertainty paradigms through a multi-scenario simulation context. Experimental analysis validates the superiority of the proposed algorithm against eight established competitors, demonstrating its effectiveness in solving complex multimodal transport problems under uncertainty. |
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| AbstractList | The evolution of the transportation industry has heightened the focus on environmentally sustainable multimodal transport, particularly in addressing carbon emissions. In modern logistics, path planning under uncertainty has become a pivotal research area. This paper proposes a multi-objective, multi-constraint optimization model for multimodal transport that aims to concurrently minimize cost, carbon emissions, and time. The model accounts for numerous operational constraints, including timetables, as well as dual sources of uncertainty from demand and the transport environment. To solve this complex problem, this paper introduces a new algorithmic framework. The proposed algorithm, a Dual-Population Constrained Multi-Objective Evolutionary Algorithm with a Success Incentive Mechanism (DSCMOEA), integrates three key innovations: a universal priority-based encoding/decoding adapter, a specialized constraint-handling architecture, and an adaptive operator selection mechanism. The adapter is central to the framework, enabling continuous-domain evolutionary algorithms to solve the discrete transport problem without internal modification. This approach also provides the versatility to handle various uncertainty paradigms through a multi-scenario simulation context. Experimental analysis validates the superiority of the proposed algorithm against eight established competitors, demonstrating its effectiveness in solving complex multimodal transport problems under uncertainty. |
| ArticleNumber | 112586 |
| Author | Zhang, Lili Deng, Libao Li, Chunlei Yang, Zhe Qin, Yifan Di, Yuanzhu |
| Author_xml | – sequence: 1 givenname: Zhe surname: Yang fullname: Yang, Zhe organization: School of Information Science and Engineering, Harbin Institute of Technology, 264209, Weihai, China – sequence: 2 givenname: Libao orcidid: 0000-0003-0076-4054 surname: Deng fullname: Deng, Libao email: dlb@hit.edu.cn organization: School of Information Science and Engineering, Harbin Institute of Technology, 264209, Weihai, China – sequence: 3 givenname: Yuanzhu surname: Di fullname: Di, Yuanzhu organization: School of Information Science and Engineering, Harbin Institute of Technology, 264209, Weihai, China – sequence: 4 givenname: Chunlei orcidid: 0000-0002-5102-0330 surname: Li fullname: Li, Chunlei organization: School of Information Science and Engineering, Harbin Institute of Technology, 264209, Weihai, China – sequence: 5 givenname: Yifan surname: Qin fullname: Qin, Yifan organization: School of Information Science and Engineering, Harbin Institute of Technology, 264209, Weihai, China – sequence: 6 givenname: Lili orcidid: 0000-0002-2203-2949 surname: Zhang fullname: Zhang, Lili organization: School of Computing, Dublin City University, Dublin D09 V209, Ireland |
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| Keywords | Multimodal transportation Constrained Multi-Objective Evolutionary Algorithms Constrained Multi-Objective Optimization Problems Constraint handling techniques |
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