A reliable facility location design model with site-dependent disruption in the imperfect information context

• Published in: PloS one Vol. 12; no. 5; p. e0177104
• Main Authors: Yun, Lifen, Wang, Xifu, Fan, Hongqiang, Li, Xiaopeng
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 09.05.2017; Public Library of Science (PLoS)
• Subjects: Communication; Costs and Cost Analysis; Customer services; Decision making; Design; Disruption; Earth Sciences; Ecology and Environmental Sciences; Engineering and Technology; Environmental engineering; Facilities management; Facility Design and Construction - economics; Industrial location; Integer programming; Medicine and Health Sciences; Methods; Modelling; Models, Theoretical; Operations research; People and Places; Production planning; Research and Analysis Methods; Seismic engineering; Sensors; Social Sciences; Traffic congestion; Beijing China; United States > US; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0177104

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. 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 proposed model aims to balance initial facility investment and expected long-term operational cost by finding the optimal facility locations. A nonlinear integer programming model is proposed to describe this problem. We apply a linearization technique to reduce the difficulty of solving the proposed model. A number of problem instances are studied to illustrate the performance of the proposed model. 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.