A stochastic program to evaluate disruption mitigation investments in the supply chain

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Supply chain risk management is becoming increasingly important due to a variety of natural and man-made uncertainties. We develop a methodology to evaluate the costs of disruptions and the value of supply chain network mitigation options based on a two-stage stochastic program. To solve the model, we rely on a solution scheme based on sample average approximation. We explicitly differentiate between disruption periods and business as usual periods to decrease the model size and computational requirements by approximately 85% and 95%, respectively. Furthermore, the decrease in model complexity allows us to include the conditional value at risk in the objective function to incorporate the risk aversion of decisions makers. Based on a case study of a chemical supply chain, this study shows the trade-off between long-term expected costs minimization and short term risk minimization, where the latter leads to a more aggressive investment policy.

LanguageEnglish
Pages516-530
JournalEuropean Journal of Operational Research
Volume274
Issue number2
DOIs
StatePublished - 16 Apr 2019

Fingerprint

Supply Chain
Supply chains
Evaluate
Sample Average Approximation
Conditional Value at Risk
Cost Minimization
Decrease
Model Complexity
Risk Aversion
Supply chain management
Risk Management
Risk management
Differentiate
Costs
Objective function
Trade-offs
Uncertainty
Methodology
Requirements
Term

Keywords

  • Stochastic programming
  • Supply chain network design
  • Supply chain risk management

Cite this

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abstract = "Supply chain risk management is becoming increasingly important due to a variety of natural and man-made uncertainties. We develop a methodology to evaluate the costs of disruptions and the value of supply chain network mitigation options based on a two-stage stochastic program. To solve the model, we rely on a solution scheme based on sample average approximation. We explicitly differentiate between disruption periods and business as usual periods to decrease the model size and computational requirements by approximately 85{\%} and 95{\%}, respectively. Furthermore, the decrease in model complexity allows us to include the conditional value at risk in the objective function to incorporate the risk aversion of decisions makers. Based on a case study of a chemical supply chain, this study shows the trade-off between long-term expected costs minimization and short term risk minimization, where the latter leads to a more aggressive investment policy.",
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A stochastic program to evaluate disruption mitigation investments in the supply chain. / Snoeck, André; Udenio, Maximiliano; Fransoo, Jan C.

In: European Journal of Operational Research, Vol. 274, No. 2, 16.04.2019, p. 516-530.

Research output: Contribution to journalArticleAcademicpeer-review

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