使用自适应加权遗传算法优化多阶段及时性运输整合问题
Bowen Lv1, Bin Yang1, Ek Peng Chew2
1Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai, China.
概括
本研究引入了一种新的方法,用于在国际航运中及时整合货物,特别是在小型的多批货物运输中. 拟议的算法优化了物流网络,提高了全球中断期间的效率和集装箱利用率.
科学领域:
- 物流和供应链管理的物流和供应链管理.
- 运营研究 运营研究
- 运输科学 运输科学
背景情况:
- 国际货物整合面临着小型,多批次货运的挑战,导致延误和容器容量未充分利用.
- 由于COVID-19的流行,人们越来越需要及时,高效的国际多式联运.
- 现有的物流网络很难有效地管理合多个来源和目的地 (OD).
研究的目的:
- 定义和解决小型,多批量国际货物的多阶段定时性运输整合问题.
- 加强物流运营之间的连接,并最大限度地利用集装箱容量.
- 开发一个灵活和高效的算法,以优化国际多式联运网络.
主要方法:
- 定义了一个多阶段的及时性运输整合问题,以分离多个OD.
- 提出了一个两阶段的适应加权遗传算法,重点关注帕雷托前方空间和人口多样性.
- 进行计算实验来分析参数相关性和算法性能.
主要成果:
- 确定了参数相关性中的规律趋势,表明适当的设置会产生更好的结果.
- 证实了大流行对不同运输方式市场份额的重大影响.
- 通过与其他方法进行比较,证明了拟议方法的可行性和有效性.
结论:
- 开发的算法有效地解决了小型多批次货运的及时货物整合的挑战.
- 优化后勤网络导致连接性增加和集装箱利用率提高.
- 这项研究为优化国际运输策略提供了宝贵的见解,特别是在应对流行病等全球事件时.
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