论文标题
使用最小延迟的迪沙斯特维修工作人员分配优化
Post-Disaster Repair Crew Assignment Optimization Using Minimum Latency
论文作者
论文摘要
在整个基础设施域中，风暴和其他天气事件造成的物理损害通常需要昂贵且具有时间敏感的维修，以尽快恢复服务。尽管最近的研究使用基于代理的模型来估计维修成本，但将维修人员分配到不同位置的实施策略通常是人为驱动的或基于简单的规则。为了找到表现策略，我们继续采用基于代理的模型，但是将此问题作为组合优化问题，称为多个维修人员的最小加权潜伏期问题。我们应用了一种分区算法，该算法使用两种不同的启发式方法来平衡所有机组人员之间的目标分配，以优化维修位置的重要性或之间的旅行时间。我们在随机生成的图和来自现实世界中的城市环境中得出的数据上基于我们的算法基准测试，并表明我们的算法比现有方法提供了明显更好的作业。
Across infrastructure domains, physical damage caused by storms and other weather events often requires costly and time-sensitive repairs to restore services as quickly as possible. While recent studies have used agent-based models to estimate the cost of repairs, the implemented strategies for assignment of repair crews to different locations are generally human-driven or based on simple rules. In order to find performant strategies, we continue with an agent-based model, but approach this problem as a combinational optimization problem known as the Minimum Weighted Latency Problem for multiple repair crews. We apply a partitioning algorithm that balances the assignment of targets amongst all the crews using two different heuristics that optimize either the importance of repair locations or the travel time between them. We benchmark our algorithm on both randomly generated graphs as well as data derived from a real-world urban environment, and show that our algorithm delivers significantly better assignments than existing methods.
