一个灰狼优化算法用于多目标累积容量车辆路由问题,考虑到操作时间
Gewen Huang1,2, Yuanhang Qi3, Yanguang Cai2
1Information and Network Center, Jiaying University, Meizhou 514015, China.
Biomimetics (Basel, Switzerland)
|June 26, 2024
概括
这项研究引入了人道主义援助物流的新模式,优化车辆路线,最大限度地减少救灾等待时间和运营成本. 与现有方法相比,一种新的算法DGWO-F2OPT显著提高了效率和解决方案质量.
科学领域:
- 运营研究 运营研究
- 物流和供应链管理的物流和供应链管理.
- 灾害管理 灾害管理
背景情况:
- 人道主义援助的交付面临的挑战是有效的车辆安排,以尽量减少接收者等待时间.
- 传统的车辆路由模型往往忽略了诸如驾驶员工作量和现场决策需求等实际因素.
- 通过运行时间和采用多目标建模来评估驾驶员的强度对于实际的援助物流至关重要.
研究的目的:
- 引入一个双目标模型,考虑运行时间的多目标累积容量车辆路由问题 (MO-CCVRP-OT).
- 尽量减少灾区累积等待时间,并减少因过度救援车辆运行时间造成的额外费用.
- 开发一个高效的优化算法来解决MO-CCVRP-OT模型.
主要方法:
- 提出了一个动态灰狼优化算法,具有浮动的2-opt (DGWO-F2OPT),集成实数编码,随机键解码和ROV规则.
- 实施了动态非主导解决方案集更新策略,以提高算法性能.
- 引入了多目标改进的浮动2-opt (F2OPT) 地方搜索,以实现高效的问题解决和更快的融合.
主要成果:
- 与DGWO-F2OPT相比,DGWO-F2OPT在其乌托邦最佳解决方案的平均适应性值低6.22%.
- 在比较试验中,DGWO-F2OPT的平均健康值比NS-2OPT低16.49%.
- MO-CCVRP-OT模型显示,欧几里德距离与乌托邦点的距离比传统的CVRP-OT更接近18.72%.
结论:
- 提议的DGWO-F2OPT算法有效地解决了MO-CCVRP-OT,平衡了人道主义援助交付速度和运营成本效率.
- 新的算法在解决方案质量和融合速度方面明显优于现有方法.
- MO-CCVRP-OT为人道主义物流提供了更全面的方法,考虑了关键的现实世界的操作约束.
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