Practice of an improved many-objective route optimization algorithm in a multimodal transportation case under uncertain demand
Abstract In recent decades, multimodal transportation has played a crucial role in modern logistics and transportation systems because of its high capacity and low cost. However, multimodal transportation driven mainly by fossil fuels may result in significant carbon emissions. In addition, transpor...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-01-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01725-4 |
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| _version_ | 1823861462631710720 |
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| author | Tianxu Cui Ying Shi Jingkun Wang Rijia Ding Jinze Li Kai Li |
| author_facet | Tianxu Cui Ying Shi Jingkun Wang Rijia Ding Jinze Li Kai Li |
| author_sort | Tianxu Cui |
| collection | DOAJ |
| description | Abstract In recent decades, multimodal transportation has played a crucial role in modern logistics and transportation systems because of its high capacity and low cost. However, multimodal transportation driven mainly by fossil fuels may result in significant carbon emissions. In addition, transportation costs, transportation efficiency, and customer demand are also key factors that constrain the development of multimodal transportation. In this paper, we develop, for the first time, a many-objective multimodal transportation route optimization (MTRO) model that simultaneously considers economic cost, carbon emission cost, time cost, and customer satisfaction, and we solve it via the nondominated sorting genetic algorithm version III (NSGA-III). Second, to further improve the convergence performance, we introduce a fuzzy decision variable framework to improve the NSGA-III algorithm. This framework can reduce the search range of the optimization algorithm in the decision space and make it converge better. Finally, we conduct numerous simulation experiments on test problems to verify the applicability and superiority of the improved algorithm and apply it to MTRO problems under uncertain demand. This work fills the research gap for MTRO problems and provides guidance for relevant departments in developing transportation and decarbonization plans. |
| format | Article |
| id | doaj-art-4b1e21666883427da0af5167726adcc9 |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-4b1e21666883427da0af5167726adcc92025-02-09T13:01:22ZengSpringerComplex & Intelligent Systems2199-45362198-60532025-01-0111212210.1007/s40747-024-01725-4Practice of an improved many-objective route optimization algorithm in a multimodal transportation case under uncertain demandTianxu Cui0Ying Shi1Jingkun Wang2Rijia Ding3Jinze Li4Kai Li5School of Management, China University of Mining and Technology (Beijing)School of Management, China University of Mining and Technology (Beijing)School of Information, Beijing Wuzi UniversitySchool of Management, China University of Mining and Technology (Beijing)School of Management, China University of Mining and Technology (Beijing)School of Information, Beijing Wuzi UniversityAbstract In recent decades, multimodal transportation has played a crucial role in modern logistics and transportation systems because of its high capacity and low cost. However, multimodal transportation driven mainly by fossil fuels may result in significant carbon emissions. In addition, transportation costs, transportation efficiency, and customer demand are also key factors that constrain the development of multimodal transportation. In this paper, we develop, for the first time, a many-objective multimodal transportation route optimization (MTRO) model that simultaneously considers economic cost, carbon emission cost, time cost, and customer satisfaction, and we solve it via the nondominated sorting genetic algorithm version III (NSGA-III). Second, to further improve the convergence performance, we introduce a fuzzy decision variable framework to improve the NSGA-III algorithm. This framework can reduce the search range of the optimization algorithm in the decision space and make it converge better. Finally, we conduct numerous simulation experiments on test problems to verify the applicability and superiority of the improved algorithm and apply it to MTRO problems under uncertain demand. This work fills the research gap for MTRO problems and provides guidance for relevant departments in developing transportation and decarbonization plans.https://doi.org/10.1007/s40747-024-01725-4Green logisticsCarbon emissionsMultimodal transportationMany-objectiveOptimization algorithm |
| spellingShingle | Tianxu Cui Ying Shi Jingkun Wang Rijia Ding Jinze Li Kai Li Practice of an improved many-objective route optimization algorithm in a multimodal transportation case under uncertain demand Complex & Intelligent Systems Green logistics Carbon emissions Multimodal transportation Many-objective Optimization algorithm |
| title | Practice of an improved many-objective route optimization algorithm in a multimodal transportation case under uncertain demand |
| title_full | Practice of an improved many-objective route optimization algorithm in a multimodal transportation case under uncertain demand |
| title_fullStr | Practice of an improved many-objective route optimization algorithm in a multimodal transportation case under uncertain demand |
| title_full_unstemmed | Practice of an improved many-objective route optimization algorithm in a multimodal transportation case under uncertain demand |
| title_short | Practice of an improved many-objective route optimization algorithm in a multimodal transportation case under uncertain demand |
| title_sort | practice of an improved many objective route optimization algorithm in a multimodal transportation case under uncertain demand |
| topic | Green logistics Carbon emissions Multimodal transportation Many-objective Optimization algorithm |
| url | https://doi.org/10.1007/s40747-024-01725-4 |
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