Distributed Learning and Control for Manufacturing Systems Scheduling

Authors:
Joonki Hong;Vittal Prabhu
Affiliations:
-;-
Venue:
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Year:
2001

Citing 4
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Abstract

A new distributed learning and control (DLC) approach is presented in this paper which integrates part-driven distributed arrival time control (DATC) and machine-driven distributed reinforcement learning control. This approach is suitable for just-in-time (JIT) production for multi-objective scheduling problem in dynamically changing shop floor environment. While part controllers are adjusting their associated parts' arrival time to minimize due-date deviation, machine controllers equipped with learning components are searching for optimal dispatching policies. The machines' control problem is modeled as Semi Markov Decision Process (SMDP) and solved using one-step Q-learning. The DLC algorithms are evaluated for minimizing the sum of duedate deviation cost and setup cost using simulation. Results show that DLC algorithms achieve significant performance improvement over usual dispatching rules in complex real-time shop floor control problems for JIT production.