优化基于预约机制的外部集装箱交付和接收时间表
Pengfei Huang1, Hao Wang1, Fangjiao Tan1
1Navigation College, Jimei University, Xiamen, Fujian, China.
PloS one
|February 21, 2025
概括
本研究介绍了一种改进的殖民地优化 (IACO) 算法,用于外部卡车调度,以降低港口运营成本. IACO算法大大降低了成本,提高了集装箱物流效率,解决了高峰时间的拥堵问题.
科学领域:
- 运营研究 运营研究
- 物流管理物流管理
- 人工智能的人工智能
背景情况:
- 港口运输效率至关重要,卡车司机在高峰时段的协调是最大的挑战.
- 现有的研究往往忽视了集装箱运输的双向性质,主要关注单向物流.
- 同时的进出口集装箱运输需要先进的调度解决方案.
研究的目的:
- 通过优化的外部卡车调度模型,尽量降低卡车公司的运营成本.
- 开发一种适应性群优化 (IACO) 算法,用于动态卡车调度.
- 通过解决高峰时间拥堵和改善卡车周转率来提高港口物流效率.
主要方法:
- 开发了外部卡车调度的优化模型,包括软时间窗口和预约反机制.
- 引入了自适应性殖民地优化 (IACO) 算法,以时间窗口影响和偏差因子增强传统的 ACO.
- 集成的粒子群优化 (PSO) 用于IACO算法的自动参数调整.
主要成果:
- 与传统的ACO相比,IACO算法减少了10.96%的卡车运营成本,与可变社区搜索相比,降低了3.02%.
- 与手动调度相比,观察到4.89%的成本降低,证明了IACO的有效性.
- 改进的算法在案例研究中显示出卓越的优化能力和稳定性.
结论:
- 适应性殖民地优化算法有效降低车队成本,提高卡车周转效率.
- 通过在时间窗口内优化卡车任务分配,IACO算法减轻了港口拥堵.
- 这种方法提高了集装箱物流的整体效率,在港口运营中实现了峰值剃须和山谷填充.
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