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Facility Location Decisions with Random Disruptions and Imperfect Estimation

Author

Listed:
  • Michael K. Lim

    (Department of Business Administration, University of Illinois at Urbana–Champaign, Champaign, Illinois 61820)

  • Achal Bassamboo

    (Department of Managerial Economics and Decision Sciences, Northwestern University, Evanston, Illinois 60208)

  • Sunil Chopra

    (Department of Managerial Economics and Decision Sciences, Northwestern University, Evanston, Illinois 60208)

  • Mark S. Daskin

    (Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109)

Abstract

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.

Suggested Citation

  • Michael K. Lim & Achal Bassamboo & Sunil Chopra & Mark S. Daskin, 2013. "Facility Location Decisions with Random Disruptions and Imperfect Estimation," Manufacturing & Service Operations Management, INFORMS, vol. 15(2), pages 239-249, May.
    • Handle: RePEc:inm:ormsom:v:15:y:2013:i:2:p:239-249
    DOI: 10.1287/msom.1120.0413
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    References listed on IDEAS

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