An application of interactive fuzzy optimization model for redesigning supply chain for resilience

Area of Science:

• Operations Research
• Supply Chain Management
• Risk Management

Background:

• Global supply chains face increasing disruptions, necessitating strategies for resilience and profitability.
• Traditional models often prioritize profit maximization, potentially overlooking disruption risks.
• Assessing and mitigating supply chain risk is crucial for sustained business operations.

Purpose of the Study:

• To design a global supply chain network that maximizes profit while minimizing disruption risk.
• To develop a bi-criteria model integrating profit and supply density for robust network design.
• To evaluate the performance of the proposed model against traditional profit-focused approaches.

Main Methods:

• A four-stage global supply chain network was modeled.
• Disruption risk was quantified using supply density, based on geographic diversification and interstage distances.
• A bi-criteria mixed-integer linear programming model was developed to maximize profit and supply density.
• An interactive fuzzy optimization algorithm was employed to solve the bi-criteria model.
• Disruption analysis was performed using discrete scenarios.

Main Results:

• The bi-criteria model achieved a 2% higher expected profit under disruption compared to the traditional model.
• The proposed network design demonstrated a 2.2% lower profit variance during disruptions.
• The interactive fuzzy optimization algorithm effectively generated efficient frontiers for decision-making.

Conclusions:

• The bi-criteria supply chain design model offers a superior balance between profit and risk mitigation.
• Network designs prioritizing both profit and supply density enhance resilience against disruptions.
• The model provides a valuable tool for firms to assess mitigation costs versus benefits.