Multi-Port Liner Ship Routing and Scheduling Optimization Using Machine Learning Forecast and Branch-And-Price Algorithm
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| Publicado en: | Journal of Marine Science and Engineering vol. 13, no. 11 (2025), p. 2163-2193 |
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| Autor principal: | |
| Otros Autores: | , , , |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 024 | 7 | |a 10.3390/jmse13112163 |2 doi | |
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| 045 | 2 | |b d20251101 |b d20251130 | |
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| 100 | 1 | |a Cao Zhichao |u School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China; caozhichao@bjtu.edu.cn (Z.C.); 2333320021@stmail.ntu.edu.cn (T.Q.); 2433110163@stmail.ntu.edu.cn (Y.T.) | |
| 245 | 1 | |a Multi-Port Liner Ship Routing and Scheduling Optimization Using Machine Learning Forecast and Branch-And-Price Algorithm | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This study focuses on an integrated three-level multi-port liner ship vessel routing and scheduling optimization problem. Specifically, the three-level multi-port network consists of hub ports, feeder ports, and cargo source points, which provide the demands’ loading/unloading at each port. Considering vessel-specific constraints such as speed, capacity, and cost, we formulate the multi-port liner ship routing and scheduling optimization problem as a mixed integer linear programming model with the objective of minimizing total voyage cost and operating time. First, we employ machine learning models to forecast the short-term demand at different ports as the input. There are multiple feasible routes generated and allowed to be elected. Second, to ensure both computational efficiency and solution quality, we devise and compare genetic algorithm (GA), simulated annealing (SA), Gurobi and the branch-and-price (B&P) algorithm to optimize scheduling plans. Experimental results demonstrate that the proposed predict-then-optimization framework effectively addresses the complexity of multi-port scheduling and routing problems, achieving a reduction in total transportation cost by 0.81% to 8.08% and a decrease in computation time by 16.86% to 24.7% compared to baseline methods, particularly with the SA + B&P hybrid approach. This leads to overall efficiency and cost-saving ocean vessel operations. | |
| 653 | |a Sea vessels | ||
| 653 | |a Linear programming | ||
| 653 | |a Integer programming | ||
| 653 | |a Accuracy | ||
| 653 | |a Ports | ||
| 653 | |a Deep learning | ||
| 653 | |a Collaboration | ||
| 653 | |a Adaptability | ||
| 653 | |a Forecasting | ||
| 653 | |a Algorithms | ||
| 653 | |a Wind farms | ||
| 653 | |a Traffic flow | ||
| 653 | |a Machine learning | ||
| 653 | |a Operating costs | ||
| 653 | |a Energy consumption | ||
| 653 | |a Learning algorithms | ||
| 653 | |a Efficiency | ||
| 653 | |a Scheduling | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Network management systems | ||
| 653 | |a Costs | ||
| 653 | |a Optimization | ||
| 653 | |a Mixed integer | ||
| 653 | |a Simulated annealing | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Qian Tao |u School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China; caozhichao@bjtu.edu.cn (Z.C.); 2333320021@stmail.ntu.edu.cn (T.Q.); 2433110163@stmail.ntu.edu.cn (Y.T.) | |
| 700 | 1 | |a Zhang Silin |u School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China; caozhichao@bjtu.edu.cn (Z.C.); 2333320021@stmail.ntu.edu.cn (T.Q.); 2433110163@stmail.ntu.edu.cn (Y.T.) | |
| 700 | 1 | |a Song, Haibo |u CRRC Intelligent Transportation Engineering Technology Co., Ltd., Beijing 610041, China; songhaibo@crrcitet.com | |
| 700 | 1 | |a Tian Yaxin |u School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China; caozhichao@bjtu.edu.cn (Z.C.); 2333320021@stmail.ntu.edu.cn (T.Q.); 2433110163@stmail.ntu.edu.cn (Y.T.) | |
| 773 | 0 | |t Journal of Marine Science and Engineering |g vol. 13, no. 11 (2025), p. 2163-2193 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3275540655/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3275540655/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3275540655/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |