The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All relevant data are within the paper and its Supporting Information file.įunding: This research is supported in part by National Natural Science Foundation of China (71601014) to LY, the Fundamental Research Funds for the Central Universities (2016JBM026) to LY, and the US National Science Foundation (1541130, 15588355) to XL. Received: DecemAccepted: ApPublished: May 9, 2017Ĭopyright: © 2017 Yun et al. PLoS ONE 12(5):Įditor: Xiaolei Ma, Beihang University, CHINA The results indicate that our proposed model can reveal a number of interesting insights into the facility location design with site-dependent disruptions, including the benefit of backup facilities and system robustness against variation of the loss-of-service penalty.Ĭitation: Yun L, Wang X, Fan H, Li X (2017) A reliable facility location design model with site-dependent disruption in the imperfect information context.

A number of problem instances are studied to illustrate the performance of the proposed model. We apply a linearization technique to reduce the difficulty of solving the proposed model. A nonlinear integer programming model is proposed to describe this problem. This proposed model aims to balance initial facility investment and expected long-term operational cost by finding the optimal facility locations. In the imperfect information contexts, customers adopt a realistic “trial-and-error” strategy to visit facilities i.e., they visit a number of pre-assigned facilities sequentially until they arrive at the first operational facility or give up looking for the service. This paper proposes a reliable facility location design model under imperfect information with site-dependent disruptions i.e., each facility is subject to a unique disruption probability that varies across the space.

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