通过混合深度强化学习和优化算法来改善医疗废物管理的车辆路由
Norhan Khallaf1, Osama Abd-El Rouf1, Abeer D Algarni2
1Machine Learning Department, Faculty of Artificial Intelligence, Menoufia University, Menoufia, Egypt.
Frontiers in artificial intelligence
|February 27, 2025
概括
人工智能优化了医疗废物运输路线,大大减少了车辆数量,最大限度地提高了运输能力. 这种智能系统提高了废物管理的效率和环境可持续性.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 运营研究 运营研究
背景情况:
- 医疗废物管理在有效的运输和处置方面面临着挑战.
- 智能系统,特别是人工智能,为优化物流和减少环境影响提供了解决方案.
研究的目的:
- 开发和评估AI算法,以优化医疗废物运输路线.
- 改善车辆容量利用率,并尽量减少医疗废物物流中的车队规模.
主要方法:
- 训练有素的人工智能算法 (Q学习,深度Q网络) 使用均的代理系统.
- 应用了封闭容量车辆路由问题框架和K-means集群用于区域划分.
- 集成混合A*-Deep Q网络和分数Knapsack动态编程与Deep Q网络进行路线和容量优化.
主要成果:
- 与Deep Q网络相结合的Fractional Knapsack需要最少的车辆 (4),与其他算法相比,车队规模减少了20-42.86%.
- 实现了100%的车辆容量利用率,优于仅使用33-66%的方法.
- 由于更长的路线改善了容量利用,观察到距离增加了9%.
结论:
- 带有深度 Q 网络的分量快囊算法是最优的医疗废物管理,当最小化车队规模和最大化容量是关键时.
- 这种人工智能驱动的方法提高了医疗废物物流中的运营效率和环境可持续性.
- 该研究强调了路线距离和车队优化之间的权衡,强调了需要量身定制的解决方案.
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