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On the risk-averse optimization of service level in a supply chain under disruption risks

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  • Tadeusz Sawik

Abstract

The worst-case optimization of service level in the presence of supply chain disruption risks is considered for the two different service levels measures: the expected worst-case demand fulfillment rate and the expected worst-case order fulfillment rate. The optimization problem is formulated as a joint selection of suppliers and stochastic scheduling of customer orders under random disruptions of supplies. The suppliers are located in different geographic regions and the supplies are subject to random local and regional disruptions. The obtained combinatorial stochastic optimization problem is formulated as a mixed integer program with conditional service-at-risk as a worst-case service level measure. The risk-averse solutions that optimize the worst-case performance of a supply chain are compared for the two service level measures. In addition, to demonstrate the impact on the cost in the process of optimizing the worst-case service level, a joint optimization of expected cost and conditional service-at-risk using a weighted-sum approach is considered and illustrated with numerical examples. The findings indicate that the worst-case order fulfillment rate shows a higher service performance than the worst-case demand fulfillment rate. Maximization of the expected worst-case fraction of fulfilled customer orders better mitigates the impact of disruption risks. The supply portfolio is more diversified and the expected worst-case fraction of fulfilled orders is greater for most confidence levels. Finally, the results clearly show that worst-case service level is in opposition to cost.

Suggested Citation

  • Tadeusz Sawik, 2016. "On the risk-averse optimization of service level in a supply chain under disruption risks," International Journal of Production Research, Taylor & Francis Journals, vol. 54(1), pages 98-113, January.
    • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:1:p:98-113
    DOI: 10.1080/00207543.2015.1016192
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    Cited by:

    1. Vimal K.E.K & Simon Peter Nadeem & Mahadharsan Ravichandran & Manavalan Ethirajan & Jayakrishna Kandasamy, 2022. "Resilience strategies to recover from the cascading ripple effect in a copper supply chain through project management," Operations Management Research, Springer, vol. 15(1), pages 440-460, June.
    2. Golmohammadi, Amirmohsen & Hassini, Elkafi, 2019. "Capacity, pricing and production under supply and demand uncertainties with an application in agriculture," European Journal of Operational Research, Elsevier, vol. 275(3), pages 1037-1049.
    3. Dmitry Ivanov, 2022. "Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic," Annals of Operations Research, Springer, vol. 319(1), pages 1411-1431, December.
    4. Lijing Zhu & Xiaohang Ren & Chulung Lee & Yumeng Zhang, 2017. "Coordination Contracts in a Dual-Channel Supply Chain with a Risk-Averse Retailer," Sustainability, MDPI, vol. 9(11), pages 1-21, November.
    5. Xiongyong Zhou & Zhiduan Xu, 2018. "An Integrated Sustainable Supplier Selection Approach Based on Hybrid Information Aggregation," Sustainability, MDPI, vol. 10(7), pages 1-49, July.
    6. Dmitry Ivanov & Alexandre Dolgui & Boris Sokolov & Marina Ivanova, 2017. "Literature review on disruption recovery in the supply chain," International Journal of Production Research, Taylor & Francis Journals, vol. 55(20), pages 6158-6174, October.
    7. Tadeusz Sawik, 2018. "Selection of a dynamic supply portfolio under delay and disruption risks," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 760-782, January.
    8. Yılmaz, Ömer Faruk & Yeni, Fatma Betül & Gürsoy Yılmaz, Beren & Özçelik, Gökhan, 2023. "An optimization-based methodology equipped with lean tools to strengthen medical supply chain resilience during a pandemic: A case study from Turkey," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    9. Ahmed Mohammed & Irina Harris & Anthony Soroka & Mohamed Naim & Tim Ramjaun & Morteza Yazdani, 2021. "Gresilient supplier assessment and order allocation planning," Annals of Operations Research, Springer, vol. 296(1), pages 335-362, January.
    10. Turan, Hasan Hüseyin & Atmis, Mahir & Kosanoglu, Fuat & Elsawah, Sondoss & Ryan, Michael J., 2020. "A risk-averse simulation-based approach for a joint optimization of workforce capacity, spare part stocks and scheduling priorities in maintenance planning," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    11. Wissuwa, Florian & Durach, Christian F. & Choi, Thomas Y., 2022. "Selecting resilient suppliers: Supplier complexity and buyer disruption," International Journal of Production Economics, Elsevier, vol. 253(C).
    12. Han Zhao & Hui Wang & Wei Liu & Shiji Song & Yu Liao, 2021. "Supply Chain Coordination with a Risk-Averse Retailer and the Call Option Contract in the Presence of a Service Requirement," Mathematics, MDPI, vol. 9(7), pages 1-19, April.
    13. Sawik, Tadeusz, 2022. "Stochastic optimization of supply chain resilience under ripple effect: A COVID-19 pandemic related study," Omega, Elsevier, vol. 109(C).
    14. Hosseini, Seyedmohsen & Barker, Kash, 2016. "A Bayesian network model for resilience-based supplier selection," International Journal of Production Economics, Elsevier, vol. 180(C), pages 68-87.
    15. Aldrighetti, Riccardo & Battini, Daria & Ivanov, Dmitry & Zennaro, Ilenia, 2021. "Costs of resilience and disruptions in supply chain network design models: A review and future research directions," International Journal of Production Economics, Elsevier, vol. 235(C).

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