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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
Autor principal: Cao Zhichao
Otros Autores: Qian Tao, Zhang Silin, Song, Haibo, Tian Yaxin
Publicado:
MDPI AG
Materias:
Sea vessels
Linear programming
Integer programming
Accuracy
Ports
Deep learning
Collaboration
Adaptability
Forecasting
Algorithms
Wind farms
Traffic flow
Machine learning
Operating costs
Energy consumption
Learning algorithms
Efficiency
Scheduling
Genetic algorithms
Artificial intelligence
Network management systems
Costs
Optimization
Mixed integer
Simulated annealing
Optimization algorithms
Economic
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:2077-1312
DOI:10.3390/jmse13112163
Fuente:Engineering Database