使用启发式方法和蚁群优化的救护车部署和重新部署-案例研究

Meryam Benabdouallah, Chakib Bojji, Othmane El Yaakoubi
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引用次数: 8

摘要

院前物流的管理是一些研究者所关注的问题。这是由于医疗保健对城市发展的巨大影响。因此,优化应急交通有助于智慧城市的发展。本文包括文献中存在的覆盖问题,并解决了救护车的分配,以覆盖摩洛哥卡萨布兰卡地区的部门,并尽量减少紧急干预的延迟。我们的工作提出了启发式方法和ACO“蚁群优化”算法之间的比较。以卡萨布兰卡地区现有应急地点的实际数据为例。因此,蚁群算法与有指导的局部搜索相结合,可在可能的等待地点(医院和消防站)分配救护车,并最大限度地减少紧急干预的总延误时间。蚁群算法的结果优于启发式算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deployment and redeployment of ambulances using a heuristic method and an Ant Colony Optimization — Case study
The management of pre hospital logistics is addressed by several researchers. That is due to the big impact that has healthcare around the city development. Thus, optimizing emergency traffic helps to smart cities growth. This paper includes coverage problems existing in literature and addresses the ambulance allocation to cover sectors in Casablanca region of Morocco and minimize the lateness of emergency intervention. Our work proposes a comparison between a heuristic method and an ACO ‘Ant Colony Optimization’ algorithm. Instances are given by real data of the existing emergency location in Casablanca region. As a result, the ACO hybridized by a guided local search provides a distribution of ambulances at potential waiting site (hospital and fire station), and minimizes the total lateness of emergency intervention. The ACO gives best results than the heuristic.
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