Dual-Channel Global Closed-Loop Supply Chain Network Optimization Based on Random Demand and Recovery Rate

Area of Science:

• Operations Research
• Supply Chain Management
• Sustainability Science

Background:

• Globalization presents challenges in predicting customer demand and managing product recycling.
• Effective inventory management is crucial to avoid shortages and reduce supply chain risks.
• Closed-loop supply chains are essential for sustainability but complex to design and plan.

Purpose of the Study:

• To analyze integrated supply chain decision-making in environments with random product demand and returns.
• To propose a multi-objective optimization model for global closed-loop supply chain design and planning.
• To balance economic, environmental, and social objectives within the supply chain.

Main Methods:

• Development of a multi-period, single-product, multi-objective mixed integer linear programming model.
• Integration of strategic decisions including network structure, capacities, product flow, and inventory levels.
• Definition of objective functions for net present value (NPV), CO2e emissions, and social sustainability indicators.

Main Results:

• The proposed model effectively addresses strategic decision problems in global closed-loop supply chains.
• Numerical examples and sensitivity analysis demonstrate the model's advantages.
• Product demand and return rates significantly impact economic and social performance.

Conclusions:

• The multi-objective optimization model is a valuable tool for designing and planning sustainable global closed-loop supply chains.
• Balancing economic, environmental, and social factors is key to mitigating supply chain risks.
• Understanding the impact of demand and return variability is critical for supply chain resilience.