Facility Location Decisions with Random Disruptions and Imperfect Estimation

• 成果类型: Article
• 署名作者: Lim, Michael K.; Bassamboo, Achal; Chopra, Sunil; Daskin, Mark S.
• 署名单位: University of Illinois System; University of Illinois Urbana-Champaign; Northwestern University; University of Michigan System; University of Michigan
• 刊物名称: M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1523-4614
• DOI: 10.1287/msom.1120.0413
• 发表日期: 2013
• 页码: 239-249
• 关键词: logistics and transportation; Supply Chain Disruptions; facility network design; estimation error; correlated disruptions; continuous approximation

摘要

Supply chain disruptions come with catastrophic consequences in spite of their low probability of occurrence. In this paper, we consider a facility location problem in the presence of random facility disruptions where facilities can be protected with additional investments. Whereas most existing models in the literature implicitly assume that the disruption probability estimate is perfectly accurate, we investigate the impact of misestimating the disruption probability. Using a stylized continuous location model, we show that underestimation in disruption probability results in greater increase in the expected total cost than overestimation. In addition, we show that, when planned properly, the cost of mitigating the misestimation risk is not too high. Under a more generalized setting incorporating correlated disruptions and finite capacity, we numerically show that underestimation in both disruption probability and correlation degree result in greater increase in the expected total cost compared to overestimation. We, however, find that the impact of misestimating the correlation degree is much less significant relative to that of misestimating the disruption probability. Thus, managers should focus more on accurately estimating the disruption probability than the correlation.