MCAH-ACO:一个多标准的自适应混合殖民地优化,用于最后一英里交付车辆路由
De-Tian Chu1, Xin-Yu Cheng1, Lin-Yuan Bai1
1Field Engineering College, Army Engineering University of PLA, Nanjing 210007, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了一种新的多标准自适应混合群优化 (MCAH-ACO) 算法,用于交付路由. 它通过考虑不仅仅是距离或时间之外的多个因素,显著降低了总成本和安全事故.
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:
- 最后一英里的交付效率对于物流至关重要,因为传统的单一标准路由模型面临着挑战.
- 现有的模型往往忽略了车辆路线中的关键社会,环境和安全因素.
- 多重旅行销售员问题 (MTSP) 是解决交付路由挑战的关键公式.
研究的目的:
- 开发一种新的多标准自适应混合群优化 (MCAH-ACO) 算法,用于配送车辆的路由.
- 将包括距离,时间,社会环境影响和安全在内的多个优化目标整合到一个全面的成本函数中.
- 通过在多目标考虑中解决它们的局限性来增强现有的路由优化方法.
主要方法:
- 为多个旅行销售员问题 (MTSP) 提出了一种多标准自适应混合群优化 (MCAH-ACO) 算法.
- 引入了多个标准的激素分解策略,每个目标都有单独的矩阵.
- 实施了适应性权重平衡机制以进行动态的标准调整,并集成了2-opt本地搜索与精英档案多样性保护.
- 开发了一个综合性成本函数,包括距离,时间,社会环境影响和安全因素.
主要成果:
- 与基线算法相比,MCAH-ACO实现了总成本减少12.3%.
- 与表现最好的基线相比,安全关键事件减少了18.7%.
- 保持了计算效率,同时超过了遗传算法 (GA),自适应GA和标准的Max-Min系统 (MMAS).
结论:
- 拟议的MCAH-ACO算法通过有效平衡多个目标,为配送车辆路线提供了一种优越的方法.
- 这种多标准优化显著提高了最后一英里配送操作的经济效率和安全性.
- 对于复杂的物流路由问题,MCAH-ACO提供了一个计算效率高和强大的解决方案.
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