A Hybrid MAS-CBR Framework with Optimization for Adaptive Supply Chain Design and Management

Global supply chains are increasingly characterized by complexity, uncertainty, and vulnerability to disruptions, creating a pressing need for intelligent, adaptive systems that support decentralized decision-making and real-time control.This paper develops a new framework that integrates Multi-Agent Systems (MAS) with Case-Based Reasoning (CBR) to address these challenges.The model leverages autonomous agents representing suppliers, manufacturers, distributors, retailers, and coordinators that negotiate through defined protocols while embedding CBR mechanisms to retrieve and adapt historical supply chain cases for enhanced responsiveness.An optimization layer, guided by both agent heuristics and case-driven initial solutions, targets key objectives such as cost minimization, leadtime reduction, and resilience improvement.Simulation experiments were conducted under both static and dynamid environments with disruptions including supplier failures and demand fluctuations.Results demonstrate that the proposed framework achieves convergence up to 34-41% faster than heuristic-only baselines (p