用多模式和多标准协作约束对危险物质运输路线的智能优化方法.
1Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, 730070, China. chenfan@mail.lzjtu.cn.
Scientific reports
|March 6, 2025
概括
本研究介绍了DSNSGA3,这是一个改进的算法,用于优化危险物质运输路线. 它提高了复杂的多式联运网络中的决策准确性和稳定性.
科学领域:
- 运营研究 运营研究
- 运输科学 运输科学
- 物流管理物流管理
背景情况:
- 由于多种标准和运输方式,危险物质运输路线优化是复杂的.
- 现有的方法很难在多式联网网络中平衡不同的评估需求.
研究的目的:
- 开发一种用于多模式运输网络和多标准路线优化的新方法.
- 改善危险物质物流的决策.
主要方法:
- 制定一个三目标整数编程模型.
- 开发一种改进的多目标遗传算法 (DSNSGA3),具有基于优先级的染色体编码.
- 整合非主导分类,拥挤距离和本地搜索策略.
- 实施自动权重分配的决策方法.
主要成果:
- DSNSGA3有效地识别了多样化和分布的帕雷托前线.
- 与NSGA3相比,DSNSGA3显示了平均1.93%的精度和1.32%的强度的改善.
- 趋同的平均时间是11.33代.
结论:
- 拟议的DSNSGA3算法为危险物质路线优化提供了卓越的性能.
- 决策方法为复杂的物流提供了平衡和最终的解决方案.
- 这种方法支持在具有挑战性的运输环境中智能多标准决策.
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