Work

Multi-Port Liner Ship Routing and Scheduling Optimization Using Machine Learning Forecast and Branch-And-Price Algorithm

Year: 2025

Type: article

Abstract: 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.

Source: Journal of Marine Science and Engineering

DOI: 10.3390/jmse13112163

Authors: Zhichao Cao, Tao Qian, Silin Zhang, Haibo Song, Yaxin Tian

Institutions: Nantong University, CRRC (China)

Language: English

Topic: Maritime Ports and Logistics

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137