Locating emergency vehicles with an approximate queuing model and a meta-heuristic solution approach |
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| Institution: | 1. Department of Production Engineering, Federal University of Ouro Preto, MG, Brazil;2. Department of Production Engineering, Federal University of Sao Carlos, SP, Brazil;3. Department of Production Engineering, Federal University of Rio de Janeiro, RJ, Brazil;4. Laboratoire Genie Industriel, Ecole Centrale Paris, France;5. Department of Business Information System and Operation Management, University of North Carolina at Charlotte, NC, USA;1. Laboratoire Genie Industriel, Ecole Centrale Paris, Chatenay Malabry 92295, France;2. Programa de Engenharia de Produção, Universidade Federal do Rio de Janeiro, 21945-970 Rio de Janeiro, RJ, Brazil;3. Departamento de Engenharia de Produção, Universidade Federal de Sao Carlos, 13565-905 São Carlos, SP, Brazil;1. Urban Transport Systems Laboratory, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland;2. Department of Management Science, Lancaster University, UK;1. Industrial Engineering Department, Nuh Naci Yazgan University, 38170 Kayseri, Turkiye;2. Industrial Engineering Department, Erciyes University, 38039 Kayseri, Turkiye;3. Graduate School of Natural and Applied Sciences, Erciyes University, 38039 Kayseri, Turkiye;1. LGIPM, 12, rue Augustin Fresnel, 57070 METZ, France;2. Department of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran;3. Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran |
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| Abstract: | In this paper, the location of emergency service (ES) vehicles is studied on fully connected networks. Queuing theory is utilized to obtain the performance metrics of the system. An approximate queuing model the (AQM) is proposed. For the AQM, different service rate formulations are constructed. These formulations are tested with a simulation study for different approximation levels. A mathematical model is proposed to minimize the mean response time of ES systems based on AQM. In the model, multiple vehicles are allowed at a single location. The objective function of the model has no closed form expression. A genetic algorithm is constructed to solve the model. With the help of the genetic algorithm, the effect of assigning multiple vehicles on the mean response time is reported. |
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| Keywords: | Emergency service vehicles Vehicle location Approximate queuing model Order of districting Genetic algorithm |
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