关键词: 水路运输, 船队配置与运营调度协同优化, 动态加船算法, 干散货船队, 时空网络, 混合整数线性规划

Abstract: To address the strong coupling among multiple decision variables and the large solution scale in the joint optimization of dry bulk fleet configuration and operational scheduling, this paper develops a mixed-integer linear programming model based on a space-time network, with the objective of maximizing total system profit. A dynamic vessel addition algorithm is proposed to solve the model, which decomposes the multi-vessel joint optimization problem into a sequence of single-vessel subproblems and employs a marginal profit termination criterion to reduce computational complexity effectively. Randomly generated instances and a large-scale realistic case based on iron ore transportation are used to validate the model and algorithm and to conduct sensitivity analyses. Numerical results show that, for most test instances, the objective value deviation between the dynamic vessel addition algorithm and Gurobi is within 5%, while computation time is substantially reduced, with particularly significant advantages in large-scale cases. Fixed costs and fuel prices exhibit a threshold effect on fleet operational decisions: within a certain range, they compress profit margins without altering fleet configuration, whereas beyond the critical threshold they trigger a reduction in the number of deployed vessels and the abandonment of low-profit cargoes. In addition, system net profit is more sensitive to freight rate changes in high-volume backbone cargo categories, while freight rate fluctuations of a small number of high-value long-haul cargoes have a relatively limited impact on overall profitability.

Key words: waterway transportation, coordinated optimization of fleet configuration and operational scheduling, dynamic vessel addition algorithm, dry bulk fleet, space-time network, mixed-integer linear programming