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Reliable Flexibility Design of Supply Chains Via Extended Probabilistic Expanders

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  • Hao Shen
  • Yong Liang
  • Zuo‐Jun Max Shen
  • Chung‐Piaw Teo

Abstract

It is well‐known that adding a little flexibility to the right place is an effective strategy to improve the performance of operations in the face of demand uncertainties, to ensure high level of capacity utilization. However, given that system disruptions are ubiquitous, the legacy flexibility designs may perform poorly under disruptions to supply or capacity installations. In this study, we focus on the design of reliable and sparse flexibility structures that consistently meet a reasonable performance criterion under disruptions to both demand and supply. Specifically, we propose a class of structures termed as extended probabilistic expanders, based on the conjecture that the expansion property, rather than the global connectivity, is critical to good performance of the structures. We prove that for a system with n retailers, essentially only O(n) supply routes between suppliers and retailers are necessary to ensure good performance under disruption. In addition, we present an efficient randomized algorithm to construct extended probabilistic expanders, and demonstrate that the construction yields very good structure with the least number of edges asymptotically. We also investigate an extension to systems with structural constraints. Numerical results demonstrate that our design has not only a wide range of applications, but also better performance than a variety of known structures.

Suggested Citation

  • Hao Shen & Yong Liang & Zuo‐Jun Max Shen & Chung‐Piaw Teo, 2019. "Reliable Flexibility Design of Supply Chains Via Extended Probabilistic Expanders," Production and Operations Management, Production and Operations Management Society, vol. 28(3), pages 700-720, March.
  • Handle: RePEc:bla:popmgt:v:28:y:2019:i:3:p:700-720
    DOI: 10.1111/poms.12942
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    Cited by:

    1. Shixin Wang & Xuan Wang & Jiawei Zhang, 2021. "A Review of Flexible Processes and Operations," Production and Operations Management, Production and Operations Management Society, vol. 30(6), pages 1804-1824, June.
    2. Guo, Haidong & Wang, Shengyu & Zhang, Yu, 2021. "Supply interruption supply chain network model with uncertain demand: an application of chance-constrained programming with fuzzy parameters," LSE Research Online Documents on Economics 114936, London School of Economics and Political Science, LSE Library.
    3. Aldrighetti, Riccardo & Battini, Daria & Ivanov, Dmitry, 2023. "Efficient resilience portfolio design in the supply chain with consideration of preparedness and recovery investments," Omega, Elsevier, vol. 117(C).
    4. Rujeerapaiboon, Napat & Zhong, Yuanguang & Zhu, Dan, 2023. "Resilience of long chain under disruption," European Journal of Operational Research, Elsevier, vol. 309(2), pages 597-615.

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