Simulation-based generation of rescheduling knowledge using a cognitive architecture

Abstract

No schedule can withstand the test of time. Unexpected disruptions are ubiquitous in manufacturing shop-floors and supply chains. Automating rescheduling decisions is thus essential to increase the type and level of autonomy used to respond timely to unplanned events. Problem-specific rescheduling knowledge is often scarce or unavailable. In this work, based on the Soar cognitive architecture, simulated transitions between schedule states due to repair operators are used for generating and compiling knowledge to respond reactively to unforeseen events generated by internal and external factors such as machine breakdowns, rush orders, material shortages and the need for reprocessing operations. Rescheduling knowledge is obtained in the form of dynamic first-order logical decision rules which can be applied in real-time to repair a schedule and guarantee feasibility. The simulation-based approach is implemented using the Problem Space Computational Model formalism, which readily integrates reinforcement learning algorithms with artificial cognitive capabilities such as memorization, chunking, and reasoning in a rescheduling agent. The Soar architecture is used to learn rescheduling rules for an industrial case study.