Risk based facility location by using fault tree analysis in disaster management

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AKGÜN İ., Gümüşbuğa F., Tansel B.

OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE, vol.52, pp.168-179, 2015 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 52
  • Publication Date: 2015
  • Doi Number: 10.1016/j.omega.2014.04.003
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus
  • Page Numbers: pp.168-179
  • Keywords: Location, Risk, Fault tree analysis, Vulnerability, Disaster management, p-Center risk model, OR/MS RESEARCH, MODEL, OPERATIONS, SUPPLIES, EMERGENCY, NETWORK
  • Abdullah Gül University Affiliated: Yes


Determining the locations of facilities for prepositioning supplies to be used during a disaster is a strategic decision that directly affects the success of disaster response operations. Locating such facilities close to the disaster-prone areas is of utmost importance to minimize response time. However, this is also risky because the facility may be disrupted and hence may not support the demand point(s). In this study, we develop an optimization model that minimizes the risk that a demand point may be exposed to because it is not supported by the located facilities. The purpose is to choose the locations such that a reliable facility network to support the demand points is constructed. The risk for a demand point is calculated as the multiplication of the (probability of the) threat (e.g., earthquake), the vulnerability of the demand point (the probability that it is not supported by the facilities), and consequence (value or possible loss at the demand point due to threat). The vulnerability of a demand point is computed by using fault tree analysis and incorporated into the optimization model innovatively. To our knowledge, this paper is the first to use such an approach. The resulting non-linear integer program is linearized and solved as a linear integer program. The locations produced by the proposed model are compared to those produced by the p-center model with respect to risk value, coverage distance, and covered population by using several test problems. The model is also applied in a real problem. The results indicate that taking the risk into account explicitly may create significant differences in the risk levels. (C) 2014 Elsevier Ltd. All rights reserved.