Multi-objective evolutionary algorithms and multiagent models for optimizing police dispatch
Multi-objective evolutionary algorithms and multiagent models for optimizing police dispatch
复制标题
用于优化警察调度的多目标进化算法和多智能体模型
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
T. Pequeno
中科院分区：
文献类型：
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作者：
Ricardo Guedes;Vasco Furtado;T. Pequeno
本文研究了多智能体模拟和多目标进化算法在公共安全资源优化配置中的应用。我们描述了一个工具，它帮助执法当局在受控环境中评估分配和调度资源的不同策略，旨在减少相互冲突的目标，如响应时间、无人值守电话数量和警车置换成本。该工具是一个多智能体模型，用于表示生活在出现紧急事件的网格中的警车。对此环境中的资源调度策略的比较表明，首先处理那些估计出勤时间较低的呼叫可以在等待时间方面提供最佳的整体性能。然而，这实际上是不可能的，因为必须确定某些犯罪类型的优先次序，导致排队等候时间增加。我们没有手动尝试确定要应用的最佳分配策略，而是将多目标进化算法耦合到模拟模型中，以便自动发现一个函数，以按满足多个目标且有时相互冲突的出勤率的最佳顺序对呼叫进行排序。
In this article we investigate Multi-agent simulation and Multi-objective Evolutionary Algorithms for optimizing resource allocation in Public Safety. We describe a tool that helps Law Enforcement authorities to evaluate, in a controlled environment, different strategies for allocating and dispatching resources, aiming at reducing conflicting goals such as response time, the number of unattended calls and cost of displacement of police cars. This tool is a multi-agent model to represent police cars that lives in a grid in which emergency occurrences appear. A comparison of the strategies for resource dispatch in this environment shows that serving first those calls with low estimated attendance times delivers the best overall performance in terms of waiting time. However this is practically impossible since prioritization of certain crime types is necessary leading to the increase of the waiting time in the queue. Instead of manually trying to identify the best allocation strategy to apply, we have coupled a multi-objective evolutionary algorithm to the simulation model in order to uncover automatically a function to rank the calls in the best order for attendance satisfying multiple and sometimes conflicting goals.