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On the risk-averse selection of resilient multi-tier supply portfolio

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  • Sawik, Tadeusz

Abstract

A multi-portfolio approach and a scenario-based stochastic mixed integer program are developed for risk-averse selection of resilient supply and demand portfolios in a geographically dispersed multi-tier supply chain network under disruption risks. The resilience of the supply chain is improved by selection of primary supply portfolio and by pre-positioning of risk mitigation inventory of parts at different tiers that will hedge against all disruption scenarios. Simultaneously for each disruption scenario, recovery and transshipment portfolios are determined and decisions on usage the pre-positioned inventory are made to minimize conditional cost-at-risk or maximize conditional service-at-risk. Some properties of optimal solutions, derived from the proposed model provide additional managerial insights. In particular, the impact of unit penalty for unfulfilled demand for products on resilience of the risk-averse supply portfolio is investigated. The findings also indicate that the developed multi-portfolio approach forms an embedded network flow structure that leads to computationally efficient stochastic mixed integer program with a very strong LP relaxation.

Suggested Citation

  • Sawik, Tadeusz, 2021. "On the risk-averse selection of resilient multi-tier supply portfolio," Omega, Elsevier, vol. 101(C).
    • Handle: RePEc:eee:jomega:v:101:y:2021:i:c:s0305048319306723
    DOI: 10.1016/j.omega.2020.102267
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    Cited by:

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    2. 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).
    3. Liu, Ming & Liu, Zhongzheng & Chu, Feng & Dolgui, Alexandre & Chu, Chengbin & Zheng, Feifeng, 2022. "An optimization approach for multi-echelon supply chain viability with disruption risk minimization," Omega, Elsevier, vol. 112(C).
    4. Tang, Lianhua & Li, Yantong & Bai, Danyu & Liu, Tao & Coelho, Leandro C., 2022. "Bi-objective optimization for a multi-period COVID-19 vaccination planning problem," Omega, Elsevier, vol. 110(C).
    5. Sawik, Tadeusz, 2022. "Stochastic optimization of supply chain resilience under ripple effect: A COVID-19 pandemic related study," Omega, Elsevier, vol. 109(C).
    6. Ming Liu & Hao Tang & Yunfeng Wang & Ruixi Li & Yi Liu & Xin Liu & Yaqian Wang & Yiyang Wu & Yu Wu & Zhijun Sun, 2023. "Enhancing Food Supply Chain in Green Logistics with Multi-Level Processing Strategy under Disruptions," Sustainability, MDPI, vol. 15(2), pages 1-21, January.
    7. (Ryan) Choi, Ji-Hung & Yoon, Jiho & Song, Ju Myung, 2023. "Adaptive R&D contract for urgently needed drugs: Lessons from COVID-19 vaccine development," Omega, Elsevier, vol. 114(C).
    8. Rozhkov, Maxim & Ivanov, Dmitry & Blackhurst, Jennifer & Nair, Anand, 2022. "Adapting supply chain operations in anticipation of and during the COVID-19 pandemic," Omega, Elsevier, vol. 110(C).
    9. Sardesai, Saskia & Klingebiel, Katja, 2023. "Maintaining viability by rapid supply chain adaptation using a process capability index," Omega, Elsevier, vol. 115(C).
    10. Hu, Shaolong & Dong, Zhijie Sasha & Lev, Benjamin, 2022. "Supplier selection in disaster operations management: Review and research gap identification," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).

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