Owing to its impact on the industrial economy, job shop scheduler and controller are vital algorithms for modern manufacturing processes. Hence a production scheduling and control that performs reactive (not deterministic) scheduling and can make decision on which job to process next based solely on its partial (not central) view of the plant becomes necessary. This requirement puts the problem in the class of agent based model (ABM). Hence this work adopts an alternative view on job-shop scheduling problem where each resource is equipped with adaptive agent that, independent of other agents makes job dispatching decision based on its local view of the plant. A combination of Markov Chain instruments and agent oriented analysis is used in the analysis of the proposed agent based model (ABM) for the job shop scheduling problem. The Markov Chain approach allows a rigorous analysis of the ABM. It provides a general framework of aggregation in agent-based and related computational models by making use of Markov Chain aggregation and lumpability theory in order to link the micro and the macro level of observation. Simulated annealing technique is used for carrying out the optimization modeling for the ABM.
Agent-Based, Scheduling, Industry, Machine Operation, Markov Chain
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