Agent-Based Modeling in Supply Chain Management:A Genetic Algorithm and Fuzzy Logic Approach

In today’s global market, reaching a competitive advantage by integrating firms in a supply chain management strategy becomes a key success for any firm seeking to survive in a complex environment. However, as interactions among agents in the supply chain management (SCM) remain unpredictable, simulation appears as a powerful tool aiming to predict market behavior and agents’ performance levels. This paper discusses the issues of supply chain management and the requirements for supply chain simulation modeling. It reviews the relationships amongArtificial Intelligence (AI) and SCM and concludes that under some conditions, SCM models exhibit some inadequacies that may be enriched by the use of AI tools. This approach aims to test the supply chain activities of nine companies in the crude oil market. The objective is to tackle the issues under which agents can coexist in a competitive environment. Furthermore, we will specify the supply chain management trading interaction amongagents by using an optimization approach based on a Genetic Algorithm (AG), Clustering and Fuzzy Logic (FL).Results support the view that the structured model provides a good tool for modeling the supply chain activities using AI methodology.