A computational study on design and performance issues of multi-agent intelligent systems for dynamic scheduling environments

Multi-Agent Intelligent Systems (MAIS) are loosely-coupled network of probl em solving systems that, whenever needed, work together with each other to dynamically solve problems that none of the system can individually solve. Among the advantages of the MAIS, when compared to the centralized systems, are increased reliability, faster problem solving, decreased communication , and more flexibility. Learning to coordinate the actions is one of the mo st important task in MAIS. In the current research, we use a widely reporte d dynamic job shop scheduling simulation model that uses distributed geneti c learning of job scheduling strategies (Pendharkar, P.C., 1997. Doctoral D issertation, Graduate School, Southern Illinois University at Carbondale; P endharkar, P.C., 1998. Distributed learning of objectives for adaptive sche duling (in review); Pendharkar, P.C., Bhattacharyya, S., 1997. Multi-agent learning in distributed artificial intelligence. Proc. 2nd INFORMS Conferen ce on Information Systems and Technology. San Diego, CA, p.156-163; Bhattac haryya, S., Koehler, G.J., 1997. Learning by objectives for adaptive shop-f loor learning. Decision Sciences (to appear). Aytug, H., Koehler, G.J., Sno wdon, J.L., 1994. Genetic learning of dynamic scheduling within a simulatio n environment, Computers and Operations Research, 21 (8), 909-925; Aytug, H ., Bhattacharyya, S., Koehler, G.J., Snowdon, J.L., 1994. A review of machi ne learning in scheduling, IEEE Transactions on Engineering Management 41 ( 2)) and study the performance and design issues in multi-agents information systems for dynamic scheduling in manufacturing. Among the design issue an d performance issues considered in this research are coordination between a gents, number of agents, and frequency of learning. Our results indicate th at coordination between agents, and learning frequency play a significant r ole in the performance of multi-agent intelligent systems. (C) 1999 Elsevie r Science Ltd. All rights reserved.