选择性车辆问题的模型,考虑混合车队与容量大的电动车辆
Jiacheng Li1, Masato Noto1, Yang Zhang2
1Department of Applied Systems and Mathematics, Kanagawa University, Yokohama, Japan.
Scientific reports
|July 2, 2025
概括
使用电动汽车 (EV) 优化最后一英里交付至关重要. 一个新的算法改善了交付完成,并减少了混合车队的能源使用,显示80%的电动汽车采用最大限度地提高了效益.
科学领域:
- 运营研究 运营研究
- 物流管理物流管理
- 可持续的运输可持续的运输
背景情况:
- 电动汽车 (EV) 的日益普及,需要为最后一英里的交付运营制定新的战略.
- 物流提供商在平衡能源消耗,交付完成率和混合车队的排放方面面临着挑战.
- 解决各种车型的自我装载和能源限制对于高效的交付至关重要.
研究的目的:
- 开发一个多目标优化模型,用于使用常规和电动汽车混合车队的最后一英里交付.
- 为了最大限度地完成服务,最大限度地减少能源消耗,减少排放.
- 提出和验证物流车队管理的先进优化算法.
主要方法:
- 制定一个考虑车辆约束和交付目标的多目标优化模型.
- 开发一个自适应的大邻里搜索 (ALNS) 算法,增强模拟和本地搜索原则.
- 使用测试实例对拟议的ALNS算法与传统优化方法进行比较分析.
主要成果:
- 与现有方法相比,拟议的ALNS算法显示出卓越的性能和计算效率.
- 该算法比传统算法实现了2%至15%的解决方案改进.
- 分析表明,要实现节能和减排的全部好处,车队中需要80%的电动汽车.
结论:
- 开发的优化模型和ALNS算法有效地解决了混合车队的最后一英里交付的复杂性.
- 车队经理可以利用这些发现来制定关于电动汽车集成和运营效率的战略决策.
- 在物流方面实现显著的环境和经济优势取决于对采用电动汽车的重大承诺.
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