4.7 Article

An analysis of p-median location problem: Effects of backup service level and demand assignment policy

期刊

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
卷 272, 期 1, 页码 207-218

出版社

ELSEVIER SCIENCE BV
DOI: 10.1016/j.ejor.2018.06.017

关键词

Location; p-median problem; Backup service; Discrete event simulation; Emergency service systems

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Any solution to facility location problems will consider determining the best suitable locations with respect to certain criteria. 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 practice, however, a demand may not be served from its nearest facility if that facility is engaged in serving other demands. This makes it a requirement to assign backup services so as to improve response time and service quality. The level of backup service is a key, strategic-level planning factor, and must be taken into consideration carefully. Moreover, in emergency service operations conducted in congested demand regions, demand assignment policy is another important factor that affects the system performance. Models failing to adopt sufficient levels of backup service and realistic demand assignment policies may significantly deteriorate the system performance. Considering 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. For this purpose, we adopt a combined optimization and simulation approach. We will first modify the classic pMP to account for distances to backup services. Next, we employ a discrete event simulation to evaluate the performance of location schemes obtained from the deterministic mathematical model. Our results provide insights for decision-makers while planning ESS operations. (C) 2018 Elsevier B.V. All rights reserved.

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