考虑能源消耗的多车型新鲜食品物流分销的时间变化的路径优化研究
Hao Chen1, Wenxian Wang2, Li Jia3
1School of Economics and Business Administration, Yibin University, Yibin, Sichuan, China.
Scientific reports
|November 7, 2024
概括
优化新鲜食品的冷藏运输可以降低成本和碳排放. 一个新的混合算法 (TS-GA) 提高了复杂,时间敏感的城市物流的效率,优于传统方法.
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
- 可持续的运输可持续的运输
背景情况:
- 新鲜食品物流面临高能耗和成本,阻碍了现代工业的需求.
- 动态的城市环境带来了随时间变化的道路拥堵和产品质量下降的挑战.
- 现有的优化方法难以应对多车辆,多点新鲜食品分发的复杂性.
研究的目的:
- 开发一个动态的城市新鲜食品物流车辆路线优化方法.
- 为了尽量减少运输成本,双重碳成本和产品损坏成本.
- 解决多车型新鲜食品在不同的时间条件下分发的NP难题.
主要方法:
- 为了优化,设计了一种混合的 Tabu Search (TS) 和 Genetic Algorithm (GA) 方法 (TS-GA).
- 车辆的速度是用零碎函数建模的,以考虑动态条件.
- 该方法全面考虑了随时间变化的道路拥堵和新鲜商品质量下降.
主要成果:
- TS-GA算法证明了对大规模分布问题的优化效率和有效性.
- 优化一个配送中心和30个配送点的网络,在16.3秒内产生了12934.02元人民币的总成本.
- 与用于多车辆,多点场景的传统遗传算法相比,TS-GA算法将整体成本降低了2.94-7.68%.
结论:
- 提出的动态城市新鲜食品物流优化方法有效降低成本并提高效率.
- 混合TS-GA算法是解决新鲜食品分销中复杂车辆路由问题的强大工具.
- 这项研究有助于为不断增长的新鲜食品市场提供可持续和具有成本效益的物流.
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