Optimization of external container delivery and pickup scheduling based on appointment mechanism

• Published in: PloS one Vol. 20; no. 2; p. e0318606
• Main Authors: Huang, Pengfei, Wang, Hao, Tan, Fangjiao, Jiang, Yuyue, Cai, Jinfen
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 21.02.2025; Public Library of Science (PLoS)
• Subjects: Adaptability; Algorithms; Analysis; Ant colony optimization; Container transportation; Containerization (Freight); Containers; Delivery scheduling; Efficiency; Fees & charges; Genetic algorithms; Humans; Logistics; Management; Models, Theoretical; Motor Vehicles; Operating costs; Optimization; Optimization models; Particle swarm optimization; Ports; Queuing; Scheduling; Scheduling (Management); Search algorithms; Services; Traffic; Transportation - economics; Transportation - methods; Trucking; Trucking industry; Trucks; Windows (intervals); China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0318606

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.