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优化家庭医疗废物回收物流路线:考虑污染风险
Jihui Hu1, Ying Zhang1, Yanqiu Liu1
1School of Management, Shenyang University of Technology, Shenyang, Liaoning, China.
PloS one
|October 7, 2024
概括
优化家庭医疗废物的物流路线将成本和污染风险降至最低. 新的自适应混合人工鱼群算法 (AH-NSAFSA) 为可持续的废物管理提供了一个生态有效的解决方案.
科学领域:
- 环境科学 环境科学
- 运营研究 运营研究
- 计算机科学 计算机科学
背景情况:
- 由于工业化和人口增长,家庭医疗废物产量正在增加.
- 有效的物流和运输对于管理这些废物和减轻环境和公共卫生风险至关重要.
- 受运输时间,体积和风力影响的污染风险,以及交通拥堵,使废物物流复杂化.
研究的目的:
- 优化家庭医疗废物物流网络中的车辆路线.
- 开发一个双目标模型,最大限度地降低运营成本和环境污染风险.
- 引入和评估一种用于复杂废物回收路径规划的新型算法.
主要方法:
- 制定一个双目标混合整数编程模型.
- 开发具有非主导分类的自适应混合人工鱼群算法 (AH-NSAFSA).
- 将污染风险,运输时间,废物量,风速和交通拥堵纳入模型.
- 使用所罗门数据集进行实证评估,并与NSAFSA和NSGA-II进行比较.
主要成果:
- AH-NSAFSA为医疗废物回收路径规划提供了可行的非主导解决方案.
- 与NSAFSA和NSGA-II.II相比,该算法在多指标决策 (MID),解决方案强度 (SM) 和解决方案数量 (NOS) 中表现优异.
- 与NSAFSA相比,AH-NSAFSA显示了更好的计算时间 (CT).
- 该研究证实了算法能够平衡成本效益与减少污染风险的能力.
结论:
- AH-NSAFSA提供了一种有效和环保的方法来优化家庭医疗废物回收路径.
- 综合模式支持企业制定可持续绿色发展战略.
- 这项研究通过优化后勤来推进可持续的医疗废物管理实践.
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