Abstract:

In recent years, advances in artificial intelligence have led to improvements in numerous tools for analysing and making decisions about complex problems. Multi-agent systems (MAS) are increasingly used in industry to solve problems related to material procurement, production scheduling, and task allocation. Introducing these systems into postgraduate education can provide students with a learning experience based on realistic scenarios of negotiation, coordination, and decision-making. This article presents a case study-based learning methodology for the design of MAS in supply chain management education. The activity allows students to design a MAS to address various supply chain problems such as purchasing and supply management (negotiation between buyer and supplier), logistics task management, and production organisation (workshop scheduling), using two or more autonomous intelligent agents. Through this methodology, students can observe how agents can be designed based on different tactics and decision-making, and analyse the results (agreements reached, profits, total cost, etc.). This approach provides a framework for experiential learning in postgraduate supply chain training, preparing students to understand and leverage emerging Industry 4.0 technologies in real-world decision-making scenarios.

Keywords:

Multi-Agent Systems, Supply Chain Management, Postgraduate Education, Case Study.