Our results provide insights for decision-makers while planning ESS operations. Next, we employ a discrete event simulation to evaluate the performance of location schemes obtained from the deterministic mathematical model. We will first modify the classic pMP to account for distances to backup services. For this purpose, we adopt a combined optimization and simulation approach. Models failing to adopt sufficient levels of backup service and realistic demand assignment policies may significantly deteriorate the system performance.Ĭonsidering the classic p-median problem (pMP) location model, this paper investigates the effects of backup service level, demand assignment policy, demand density, and number of facilities and their locations on the solution performance in terms of multiple metrics. Moreover, in emergency service operations conducted in congested demand regions, demand assignment policy is another important factor that affects the system performance. The level of backup service is a key, strategic-level planning factor, and must be taken into consideration carefully. This makes it a requirement to assign backup services so as to improve response time and service quality. In practice, however, a demand may not be served from its nearest facility if that facility is engaged in serving other demands. Among different types of location problems, involving emergency service system (ESSs) are one of the most widely studied in the literature, and solutions to these problems will mostly aim to minimize the mean response time to demands. In final, the evident absence of research in three mentioned key features in one frame has left covering literature in defect and brought about the proposed three-objective model in this paper which is called combined maximal covering with backup model (MCBM) and interval full-ranking.Īny solution to facility location problems will consider determining the best suitable locations with respect to certain criteria. As many research have proved to believe in uncertainty, no one can neglect this feature in real-world, we have just defined data in intervals to consider this feature. To overcome this problem, we have entered efficiency to our model by considering location of each facility as an input for a revised version of data envelopment analysis which is called full-ranking model. Moreover, inefficient facilities were absolutely neglected in covering literature despite their destructive role in serving customer demands. Emergency services are harshly sensible to delay, so the problem of server's unavailability should be solved by considering backup coverage. The proposed model in this paper is searching for three assumptions to cover major features.
Furthermore, by combining backup covering and interval full-ranking models (also conceptions), not only time is saved and more factors like efficiency and cost are simultaneously evaluated, but also covering considerations will be reachable in real aspects. In this research, for facing unavailability and uncertainty in input data, backup covering and interval full-ranking models are addressed, respectively.
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Therefore, how to deal with these sorts of assumptions has been always a question. Assumptions like the unavailability of servers, uncertainty, and evaluating more factors at the same time, are assumptions with which covering models are always faced however, these models are not able to find any answers for them. Accordingly, a general approach would not be able to answer the needs of encountering varied aspects of real-world considerations. But some assumptions of covering models are not realistic enough. Covering models have many applications in a wide variety of real-world problems.