一个可持续的多目标模型容量-电动-车辆-路由问题考虑硬和软时间窗口以及部分充电.
Amir Hossein Sheikh Azadi1, Mohammad Khalilzadeh2, Jurgita Antucheviciene3
1School of Industrial Engineering, College of Engineering, University of Tehran, Tehran 1417614411, Iran.
Biomimetics (Basel, Switzerland)
|April 26, 2024
概括
这项研究优化了考虑充电和部分充电的电动汽车路线 (EVRP). 混合元启发学为复杂的,多目标的交付问题提供高效的解决方案.
科学领域:
- 运营研究 运营研究
- 环境科学 环境科学
- 运输工程 运输工程
背景情况:
- 交通运输部门的高污染需要可持续的解决方案,如电动汽车 (EV).
- 电动汽车路由问题 (EVRP) 的研究越来越多,重点关注电池容量有限和运行领域等挑战.
- 现有的EVRP模型往往忽略了诸如充电站可用性和部分充电策略等实际方面.
研究的目的:
- 解决一个扩展的电动汽车路由问题 (EVRP),包括客户交付,充电站和部分充电.
- 开发一个考虑经济,环境和社会因素的多目标优化模型.
- 评估精确的和元启发式的算法来解决小规模和大规模的EVRP实例.
主要方法:
- 制定EVRP作为一个多目标整数线性编程模型.
- 预防性模糊目标编程 (PFGP) 适用于小规模的问题.
- 使用混合元启发式算法 (MOSA,MOGWO,MOPSO,NSGAII_TLBO) 来解决大型问题.
主要成果:
- 混合元启发算法有效地为EVRP生成了高质量的非主导解决方案.
- 在解决时间方面,MOSA算法表现出卓越的性能.
- 在解决方案质量指标 (MID,MOCV,HV) 中,NSGA-II-TLBO算法表现出色.
结论:
- 拟议的多目标模型和算法为实际的EVRP场景提供了高效的解决方案.
- 混合元启发学适用于解决复杂的,具有经济,环境和社会考虑的大型EVRP.
- 算法选择取决于特定的性能优先级,例如速度与解决方案质量.
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