Comparison of Manual Scheduling Results.

Port transport efficiency has become an urgent issue that needs to be improved, especially the coordination among truck drivers during peak hours. Previous studies mainly focus on one-way container transportation logistics issues, but container movements often occur simultaneously in both directions in practice. Therefore, this study aims to minimize truck companies’ operational costs by establishing an optimization model for external truck scheduling. This model takes soft time windows and an appointment feedback mechanism into consideration. Building upon the traditional Ant Colony Optimization (ACO) algorithm, this paper introduces an adaptive version of the ACO algorithm. The improved Ant Colony Optimization algorithm (IACO) incorporates a time window width impact factor and a time deviation consideration into its state transition rules, enhancing its adaptability. Furthermore, by integrating Particle Swarm Optimization (PSO), the algorithm intelligently tunes the pheromone and heuristic factors of ACO, achieving automatic parameter optimization. Through case studies, we have demonstrated the superior performance of this algorithm in addressing relevant problems. The results show that, in terms of truck operational costs, the improved algorithm reduces costs by 10.96% and 3.02% compared to traditional Ant Colony Optimization and Variable Neighborhood Search algorithms, respectively, and by 4.89% compared to manual scheduling. These results demonstrate that the adaptive Ant Colony Optimization algorithm exhibits clear advantages in optimization capability and stability. The algorithm effectively allocates truck tasks within each time window, thereby reducing fleet costs, improving truck turnover efficiency, mitigating port congestion, and ultimately enhancing container logistics efficiency, achieving the goals of peak shaving and valley filling.港口运输效率已成为亟待提升的紧迫问题，高峰时段集卡司机的协同调度更是其中的突出难点。过往研究多聚焦于单向集装箱运输物流问题，但实际运营中集装箱往往同时存在双向流转需求。为此，本研究旨在通过构建外部集卡调度优化模型，最小化集卡企业的运营成本。该模型纳入了软时间窗与预约反馈机制两大核心要素。本文以传统蚁群优化（Ant Colony Optimization, ACO）算法为基础，提出了一种自适应改进蚁群算法。所提出的改进蚁群优化（Improved Ant Colony Optimization, IACO）算法在状态转移规则中融入了时间窗宽度影响因子与时间偏差考量，有效提升了算法的自适应能力。此外，本文通过融合粒子群优化（Particle Swarm Optimization, PSO）算法，实现了对蚁群优化算法信息素与启发式因子的智能调节，完成了参数的自动优化。通过案例仿真验证，本算法在相关问题求解中展现出更优异的性能表现。实验结果表明，相较于传统蚁群优化（Ant Colony Optimization, ACO）算法与变邻域搜索（Variable Neighborhood Search, VNS）算法，本改进算法在集卡运营成本上分别降低了10.96%与3.02%；相较于人工调度模式，成本降低幅度达4.89%。上述结果证实，该自适应蚁群优化算法在优化能力与稳定性方面均具备显著优势。该算法可实现各时间窗内集卡任务的高效分配，进而降低车队运营成本、提升集卡周转效率、缓解港口拥堵，最终实现集装箱物流效率的整体提升，并达成削峰填谷的优化目标。