shopST: Flexible Job-Shop Scheduling with Agent-Based Simulated Trading

Frank Y. Nedwed, Ingo Zinnikus, Maxat Nukhayev, Matthias Klusch, Luca Mazzola

In: Proceedings of the 15th German Conference on Multiagent System Technologies (MATES). German Conference on Multiagent System Technologies (MATES-17) 15th August 23-26 Leipzig Germany Springer 2017.


Paradigms in modern production are shifting and pose new demands for optimization techniques. The emergence of new, versatile, recon gurable and networked machines enables flexible manufacturing scenarios which require, in particular, planning and scheduling methods for cyber-physical production systems to be flexible, reasonably fast, and anytime. This paper presents an approach, called shopST, to flexible job-shop manufacturing scheduling with agent-based simulated trading. Aspects of real manufacturing scheduling problems form the basis for a physical decomposition of the planning system into agents. The initial schedule created by the agents in shopST through reactive negotiation is successively improved through the exchange of resource binding constraints with an additional market agent. shopST is evaluated and discussed in comparison to recent solving algorithms in this domain.

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz