Relocatable modular capacities in risk aware strategic supply network planning under demand uncertainty

Area of Science:

• Operations Research
• Supply Chain Management
• Decision Science

Background:

• Supply network design is complex, especially with uncertain demand.
• Traditional models often fail to adequately address demand uncertainty and associated risks.
• Relocatable modular capacities offer flexibility in supply chain configurations.

Purpose of the Study:

• To develop a robust model for three-echelon supply network design under demand uncertainty.
• To incorporate Conditional Value at Risk (CVaR) for explicit risk management.
• To optimize the supply network configuration by maximizing a weighted sum of expected net present value and CVaR.

Main Methods:

• Formulation of a new mathematical model for supply network design.
• Integration of relocatable modular capacities.
• Application of Conditional Value at Risk (CVaR) to quantify and manage risk.
• Approximation of the nonlinear model using piecewise linearization.

Main Results:

• The proposed model yields a robust and stable supply network configuration.
• The model effectively balances expected financial returns with risk mitigation.
• Numerical investigations confirm the model's effectiveness in uncertain environments.

Conclusions:

• The developed model provides a robust framework for supply network design facing demand uncertainty.
• Incorporating CVaR enhances risk management in supply chain decisions.
• The approach offers practical insights for creating resilient and adaptable supply networks.