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Modeling and mitigating supply chain disruptions as a bilevel network flow problem.

René Y Glogg1, Anna Timonina-Farkas1, Ralf W Seifert1,2

  • 1École Polytechnique Fédérale de Lausanne (EPFL), EPFL-CDM-MTEI-TOM, ODY 1.03, Station 5, 1015 Lausanne, Switzerland.

Computational Management Science
|July 31, 2023
PubMed
Summary

This study introduces a bilevel optimization framework to mitigate supply chain disruptions. The model minimizes production costs by reducing manufacturer regrets, offering a strategy for rare, high-impact scenarios.

Keywords:
Benders decompositionRisk mitigationStochastic bilevel optimizationSupply chain managementSupply chain resilience

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Area of Science:

  • Operations Research
  • Supply Chain Management
  • Optimization

Background:

  • Globalization and cost-cutting measures have heightened supply chain vulnerability.
  • Effective risk mitigation strategies are crucial for modern production settings.
  • Supply chain disruptions pose significant financial risks to manufacturers.

Purpose of the Study:

  • To develop an effective risk mitigation strategy for supply chain disruptions in a production setting.
  • To minimize total production cost for a manufacturer by optimizing disruption mitigation strategies.
  • To reduce manufacturer regrets in disrupted scenarios using a novel bilevel optimization framework.

Main Methods:

  • A bilevel optimization framework is employed to model the problem.
  • The framework formulates a convex network flow program with a chance constraint on manufacturer regrets.
  • Generalized Benders decomposition and customized feasibility cuts are used for efficient problem-solving.

Main Results:

  • The research analyzes supply chain disruptions and manufacturer regrets.
  • Regrets are defined as the cost difference between reactive and anticipative production plans.
  • The proposed mitigation strategy reduces regrets for long disruptions at the expense of shorter ones.

Conclusions:

  • The study offers a method to decrease production costs, particularly under rare but high-impact disruption scenarios.
  • Managerial insights into risk-adjusted production are provided.
  • The framework effectively balances the impact of different disruption lengths on overall costs.