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A simulation-optimization approach for the facility location and vehicle assignment problem for firefighters using a loosely coupled spatio-temporal arrival process
Computers & Industrial Engineering ( IF 7.9 ) Pub Date : 2021-03-24 , DOI: 10.1016/j.cie.2021.107242
Sebastian A. Rodriguez , Rodrigo A. De la Fuente , Maichel M. Aguayo

This work proposes a framework to aid the strategic decision making regarding the proper location of fire stations as well as their assignment of vehicles to improve emergency response. We present an iterative simulation–optimization approach that based on some precomputed utilization parameters updates the optimal location of vehicles and fire stations. First, we find an optimal solution by using a robust formulation of the Facility Location and Equipment Emplacement Technique with Expected Coverage (Robust FLEET-EXC) model, which maximizes demand considering vehicles’ utilization. Second, we use this solution as an input to a discrete event simulation model to compute utilization parameters. Then, if the obtained parameters deviate less than a desired error, the solution is maintained; otherwise, a new solution is computed with these new parameters. Additionally, the emergencies arrival process is modeled by a spatio-temporal sampling method that loosely couples a Kernel Density Estimator and a non-homogeneous non-renewal arrival process with a Markov-Mixture of Erlangs of Common Order model as base process. Then, the proposed robust model is compared to a deterministic FLEET model that does not account for vehicles’ availability, and the FLEET-EXC model with simulated utilization parameters. The main results show that the proposed spatio-temporal sampling method achieves a better representation of the emergency arrival process than those generally used in literature, and the resulting utilization parameters are statistically different than those produced by a Hypercube Queueing Model. On the other hand, the simulation–optimization approach that uses the Robust FLEET-EXC has the best performance, achieving the highest coverage of emergencies in 13 out of 15 experiments. Finally, this model is statistically better than the deterministic FLEET in all but one experiment, resulting in up to 6.42% more coverage.



中文翻译:

使用松耦合时空到达过程的消防员设施定位和车辆分配问题的仿真优化方法

这项工作提出了一个框架,以协助有关消防站的正确位置及其车辆分配的战略决策,以改善应急响应。我们提出了一种迭代的仿真优化方法,该方法基于一些预先计算的利用率参数来更新车辆和消防站的最佳位置。首先,我们通过使用具有预期覆盖率的设施位置和设备安置技术(Robust FLEET-EXC)模型的稳健公式,找到了一种最佳解决方案,该模型可以最大程度地考虑车辆的利用率。其次,我们将此解决方案用作离散事件仿真模型的输入,以计算利用率参数。然后,如果所获得的参数偏差小于期望的误差,则维持解。除此以外,使用这些新参数计算出新的解决方案。此外,紧急情况到达过程是通过时空采样方法建模的,该方法将内核密度估计器和非均匀非更新到达过程与通用顺序Erlangs的Markov-Mixture松散耦合为基础过程。然后,将所提出的鲁棒模型与不考虑车辆可用性的确定性FLEET模型以及具有模拟利用率参数的FLEET-EXC模型进行比较。主要结果表明,所提出的时空采样方法比文献中常用的方法更好地表示了紧急到达过程,并且所得到的利用率参数在统计上与超立方体排队模型产生的参数不同。另一方面,使用鲁棒FLEET-EXC的仿真优化方法具有最佳性能,在15个实验中有13个实现了紧急情况的最高覆盖率。最后,除了一个实验外,该模型在统计上比确定性FLEET更好,因此覆盖率最多可提高6.42%。

更新日期:2021-05-03
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