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Selection of resilient supply portfolio under disruption risks

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  • Sawik, Tadeusz

Abstract

This paper deals with the optimal selection and protection of part suppliers and order quantity allocation in a supply chain with disruption risks. The protection decisions include the selection of suppliers to be protected against disruptions and the allocation of emergency inventory of parts to be pre-positioned at the protected suppliers. The decision maker needs to decide which supplier to select for parts delivery and how to allocate orders quantity among the selected suppliers, and which of the selected suppliers to protect against disruptions and how to allocate emergency inventory among the protected suppliers. The problem objective is to achieve a minimum cost of suppliers protection, emergency inventory pre-positioning, parts ordering, purchasing, transportation and shortage and to mitigate the impact of disruption risks by minimizing the potential worst-case cost. As a result a resilient supply portfolio is identified with protected suppliers capable of supplying parts in the face of disruption events. A mixed integer programming approach is proposed to determine risk-neutral, risk-averse or mean-risk supply portfolios, with conditional value-at-risk applied to control the risk of worst-case cost. Numerical examples are presented and some computational results are reported.

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Bibliographic Info

Article provided by Elsevier in its journal Omega.

Volume (Year): 41 (2013)
Issue (Month): 2 ()
Pages: 259-269

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Handle: RePEc:eee:jomega:v:41:y:2013:i:2:p:259-269

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Related research

Keywords: Supplier selection; Resilient supply portfolio; Disruption risks; Conditional value-at-risk; Mixed integer programming;

References

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  1. Sawik, Tadeusz, 2011. "Selection of supply portfolio under disruption risks," Omega, Elsevier, vol. 39(2), pages 194-208, April.
  2. Brian Tomlin, 2006. "On the Value of Mitigation and Contingency Strategies for Managing Supply Chain Disruption Risks," Management Science, INFORMS, vol. 52(5), pages 639-657, May.
  3. Schmitt, Amanda J., 2011. "Strategies for customer service level protection under multi-echelon supply chain disruption risk," Transportation Research Part B: Methodological, Elsevier, vol. 45(8), pages 1266-1283, September.
  4. Yu, Haisheng & Zeng, Amy Z. & Zhao, Lindu, 2009. "Single or dual sourcing: decision-making in the presence of supply chain disruption risks," Omega, Elsevier, vol. 37(4), pages 788-800, August.
  5. Berger, Paul D. & Gerstenfeld, Arthur & Zeng, Amy Z., 2004. "How many suppliers are best? A decision-analysis approach," Omega, Elsevier, vol. 32(1), pages 9-15, February.
  6. Jenkins, L., 2000. "Selecting scenarios for environmental disaster planning," European Journal of Operational Research, Elsevier, vol. 121(2), pages 275-286, March.
  7. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
  8. Rawls, Carmen G. & Turnquist, Mark A., 2010. "Pre-positioning of emergency supplies for disaster response," Transportation Research Part B: Methodological, Elsevier, vol. 44(4), pages 521-534, May.
  9. Ruiz-Torres, Alex J. & Mahmoodi, Farzad, 2007. "The optimal number of suppliers considering the costs of individual supplier failures," Omega, Elsevier, vol. 35(1), pages 104-115, February.
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Cited by:
  1. Hammami, Ramzi & Temponi, Cecilia & Frein, Yannick, 2014. "A scenario-based stochastic model for supplier selection in global context with multiple buyers, currency fluctuation uncertainties, and price discounts," European Journal of Operational Research, Elsevier, vol. 233(1), pages 159-170.
  2. Sawik, Tadeusz, 2014. "Joint supplier selection and scheduling of customer orders under disruption risks: Single vs. dual sourcing," Omega, Elsevier, vol. 43(C), pages 83-95.

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