An almost robust model for minimizing disruption exposures in supply systems

Zhao, Kena; Ng, Tsan Sheng; Tan, Chin Hon; Pang, Chee Khiang

doi:10.1016/j.ejor.2021.03.003

Cited by 3 publications

(1 citation statement)

References 34 publications

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“…Due to force majeure such as natural disasters, government interventions, and abnormal social events, supply chain enterprises may suffer from serious demand disruption (Chen and Xiao, 2009;Pi et al, 2019;Zhao et al, 2021). At the early outbreak stage of the Corona Virus Disease 2019 (COVID-19) pandemic, traditional sales experienced a serious decline in many industries, including cosmetics, catering, and apparel.…”

Section: Introductionmentioning

confidence: 99%

Recovery from demand disruption: Two-stage financing strategy for a capital-constrained supply chain under uncertainty

Zhao

Zhou

Leus

2022

European Journal of Operational Research

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Section: Introductionmentioning

confidence: 99%

Recovery from demand disruption: Two-stage financing strategy for a capital-constrained supply chain under uncertainty

Zhao

Zhou

Leus

2022

European Journal of Operational Research

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Transactive energy strategy for energy trading of proactive distribution company with renewable systems: A robust/stochastic hybrid technique

Daneshvar,

Mohammadi-Ivatloo,

Zare

et al. 2022

e-Prime - Advances in Electrical Engineering, Electronics and E

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A Two-Stage Sustainable Supplier Selection Model Considering Disruption Risk

Lu,

Li,

2024

Sustainability

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The global spread of the pandemic has changed many aspects of life and placed the supply chain at risk of disruption. To solve the problem of supplier selection under the risk of supply chain disruption, in this paper, we propose a two-stage evaluation model to address the issue of supplier selection in the context where a pandemic requires a lockdown. First, we incorporate the lead time into the epidemic model that predicts the evolution of the pandemic to identify suppliers that have a high risk of disruption caused by the pandemic’s evolution. Second, we propose a best–worst method combined with regret theory to rank candidate suppliers. Our model provides a dynamic link between the pandemic’s evolution and supplier selection, and it allows selecting suppliers according to various criteria while avoiding supply chain disruptions due to inappropriate supplier selection. We validate the proposed model on a real case study with epidemic data from China. This paper is the first to consider the impact of lockdowns during the pandemic on supplier selection. We develop a novel MCDM model BWM-RT for supplier selection; our model can be an effective decision support approach to help decision makers better cope with the risk of supply chain disruptions.

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An almost robust model for minimizing disruption exposures in supply systems

Cited by 3 publications

References 34 publications

Recovery from demand disruption: Two-stage financing strategy for a capital-constrained supply chain under uncertainty

Recovery from demand disruption: Two-stage financing strategy for a capital-constrained supply chain under uncertainty

Transactive energy strategy for energy trading of proactive distribution company with renewable systems: A robust/stochastic hybrid technique

A Two-Stage Sustainable Supplier Selection Model Considering Disruption Risk

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