Agent Based Markov Chain for Job Shop Scheduling and Control: Review of the Modeling Technigue

ABSTRACT Owing to its impact on the industrial economy, the job shop scheduler and controller are vital algorithms for modern manufacturing processes. This paper is concerned with the review of the modeling of agent based markov chain for job shop scheduling and control. The 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. Given the fact that agent based modeling is proven to be an effective way of modeling complex systems that are not easy to characterize analytically, this work is focused on addressing the JSP by developing an agent based model in which all information of the dynamics of the model is formulated as a Markov chain