An agent-based modeling framework for the design of a dynamic closed-loop supply chain network

Area of Science:

• Operations Research
• Supply Chain Management
• Computational Economics

Background:

• Supply chains involve complex material, information, and fund flows.
• Closed-loop supply chains (CLSCs) add complexity with product returns and value recovery.
• Traditional analytical methods struggle with the dynamic and uncertain nature of CLSCs.

Purpose of the Study:

• To develop an agent-based model for generic closed-loop supply chain network design.
• To integrate dynamic customer behavior and uncertainties into CLSC modeling.
• To create a more realistic CLSC structure by overcoming analytical method limitations.

Main Methods:

• Utilized agent-based modeling and simulation methodology.
• Defined actors in the CLSC network as agents with specific goals, properties, and behaviors.
• Handled dynamic customer arrivals, changing purchasing preferences, and return uncertainties within the agent-based architecture.

Main Results:

• Simulated various scenarios by adjusting parameters like supplier capacity, advertising influence, market incentives, and environmental awareness.
• Observed the impact of these parameters on CLSC performance.
• Identified the critical role of balanced stocking of new and refurbished products.

Conclusions:

• The effectiveness of advertising, incentives, and environmental awareness is significantly influenced by product stocking levels.
• Achieving desired sales amounts and cost targets requires careful management of new and refurbished product supply.
• Agent-based modeling provides a robust framework for analyzing complex CLSC dynamics.