• Title/Summary/Keyword: Probabilistic Location Set Covering Problem

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A Study of Ambulance Location Problem Applying the Iterative Procedure of Simulation and Optimization (시뮬레이션과 최적화 모형을 혼합 적용한 구급차 위치선정 모형의 해법연구)

  • Lim, Young Sun;Kim, Sun Hoon;Lee, Young Hoon
    • Journal of the Korean Operations Research and Management Science Society
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    • v.37 no.4
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    • pp.197-209
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    • 2012
  • This paper studies an emergency service vehicle location problem, where minimum reliability level pre-specified at each demand point is assured. Several models are suggested depending on the busy fraction, which is the time proportion of unavailability for the ambulances. In this paper a new model on computing the busy fraction is suggested, where it varies depending on the distance between the demand point and ambulances, hence it may respond the more realistic situation. The busy fraction for the ambulance location determined by the optimization model is computed by the simulation, and updated through the iterative procedure. It has been shown that the performances of the solutions obtained by the algorithm suggested for the instances appeared in the literature.

On an Optimal Artillery Deployment Plan (포대의 적정배치 방안)

  • Yun, Yun-Sang;Kim, Seong-Sik
    • Journal of the military operations research society of Korea
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    • v.8 no.2
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    • pp.17-30
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    • 1982
  • This paper offers an optimal artillery deployment scheme for the defending unit when two forces are confronted at a military front line. When proposed gun sites, types and number of guns as well as targets are given, the solutions of the two models in this paper direct each (unit of) guns to a certain location. The aim of the models is to maximize the number of guns which can hit important targets. Unlike widely used target assignment models, these models are formulated using the set covering problem concept. These models do not contain probabilities and time. Thus they are simple as models, easy in implementation, and yield tractable solutions. The dynamic and probabilistic feature of battle situations is implicitly reflected on the models. The first model is for the case that enemies' approaching route is clearly predictable, while the second model is for the unpredictable approaching route case.

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