Stochastic optimization of supply chain resilience under ripple effect: A COVID-19 pandemic related study
•A portfolio approach is applied for spatiotemporally integrated decision-making in a multi-regional supply chain under pandemic disruption risks.•Stochastic mixed integer programs for optimization of supply chain operations under propagated regional disruptions are developed.•Simultaneous disruptio...
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| Vydané v: | Omega (Oxford) Ročník 109; s. 102596 |
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| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
01.06.2022
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| ISSN: | 0305-0483, 1873-5274 |
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| Abstract | •A portfolio approach is applied for spatiotemporally integrated decision-making in a multi-regional supply chain under pandemic disruption risks.•Stochastic mixed integer programs for optimization of supply chain operations under propagated regional disruptions are developed.•Simultaneous disruptions in supply, demand and logistics across the entire supply chain are modeled.•The resilient measures commonly used to mitigate the impacts of region-specific disruptions are successfully applied for mitigation the impacts of multi-regional pandemic disruptions.
This paper presents a multi-portfolio approach and scenario-based stochastic MIP (mixed integer programming) models for optimization of supply chain operations under ripple effect. The ripple effect is caused by regional pandemic disruption risks propagated from a single primary source region and triggering delayed regional disruptions of different durations in other regions. The propagated regional disruption risks are assumed to impact both primary and backup suppliers of parts, OEM (Original Equipment Manufacturer) assembly plants as well as market demand. As a result, simultaneous disruptions in supply, demand and logistics across the entire supply chain is observed. The mitigation and recovery decisions made to improve the supply chain resilience include pre-positioning of RMI (Risk Mitigation Inventory) of parts at OEM plants and ordering recovery supplies from backup suppliers of parts, located outside the primary source region. The decisions are spatiotemporally integrated. The pre-positioning of RMI implemented before a disruptive event is optimized simultaneously with the RMI usage and recovery supply portfolios for the backup suppliers in the aftermath periods. The recovery supplies of parts and production at OEM plants, are coordinated under random availability of suppliers and plants and random market demand. The resilient solutions for the resilient supply portfolios are compared with the non-resilient solutions with no recovery resources available. The findings indicate that the resilient measures commonly used to mitigate the impacts of region-specific disruptions can be successfully applied for mitigation the impacts of multi-regional pandemic disruptions and the ripple effect. |
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| AbstractList | •A portfolio approach is applied for spatiotemporally integrated decision-making in a multi-regional supply chain under pandemic disruption risks.•Stochastic mixed integer programs for optimization of supply chain operations under propagated regional disruptions are developed.•Simultaneous disruptions in supply, demand and logistics across the entire supply chain are modeled.•The resilient measures commonly used to mitigate the impacts of region-specific disruptions are successfully applied for mitigation the impacts of multi-regional pandemic disruptions.
This paper presents a multi-portfolio approach and scenario-based stochastic MIP (mixed integer programming) models for optimization of supply chain operations under ripple effect. The ripple effect is caused by regional pandemic disruption risks propagated from a single primary source region and triggering delayed regional disruptions of different durations in other regions. The propagated regional disruption risks are assumed to impact both primary and backup suppliers of parts, OEM (Original Equipment Manufacturer) assembly plants as well as market demand. As a result, simultaneous disruptions in supply, demand and logistics across the entire supply chain is observed. The mitigation and recovery decisions made to improve the supply chain resilience include pre-positioning of RMI (Risk Mitigation Inventory) of parts at OEM plants and ordering recovery supplies from backup suppliers of parts, located outside the primary source region. The decisions are spatiotemporally integrated. The pre-positioning of RMI implemented before a disruptive event is optimized simultaneously with the RMI usage and recovery supply portfolios for the backup suppliers in the aftermath periods. The recovery supplies of parts and production at OEM plants, are coordinated under random availability of suppliers and plants and random market demand. The resilient solutions for the resilient supply portfolios are compared with the non-resilient solutions with no recovery resources available. The findings indicate that the resilient measures commonly used to mitigate the impacts of region-specific disruptions can be successfully applied for mitigation the impacts of multi-regional pandemic disruptions and the ripple effect. |
| ArticleNumber | 102596 |
| Author | Sawik, Tadeusz |
| Author_xml | – sequence: 1 givenname: Tadeusz surname: Sawik fullname: Sawik, Tadeusz email: sawik@ru.is, ghsawik@cyf-kr.edu.pl organization: Department of Engineering, Reykjavik University, Reykjavik IS-101, Iceland |
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| Keywords | COVID-19 pandemic disruptions Ripple effect Mixed integer programming Resilient supply portfolio Supply chain disruption management Stochastic optimization |
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| SubjectTerms | COVID-19 pandemic disruptions Mixed integer programming Resilient supply portfolio Ripple effect Stochastic optimization Supply chain disruption management |
| Title | Stochastic optimization of supply chain resilience under ripple effect: A COVID-19 pandemic related study |
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