Learning adaptive dispatching rules for a manufacturing process system by using reinforcement learning approach

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Advanced manufacturing, which employs the latest information and communication technologies to facilitate interconnected, efficient, and adaptive manufacturing systems, has become a prominent research topic in both academics and industry in recent years. One critical aspect of advanced manufacturing is how to match various complex job requirements with a manufacturer's real-time processing capabilities and its other resources within a job shop to optimally schedule manufacturing processes with multiple objectives. In general, a manufacturing scheduling problem is a non-deterministic, polynomial-time (NP)-hard problem. Due to its complexity, scheduling problems present a number of challenges to find the best possible solutions. In order to deal with a dynamic job shop scheduling problem—particularly, a dispatching problem—for a manufacturing system that is able to handle multiple product types through multi-stages and multi-machines with dynamic orders, stochastic processing time and setup time. Based on a generic framework, this research develops a solution by using a reinforcement learning-based scheduling approach that can adaptively update the production schedule by utilizing the real-time product and process events information during executions. More specifically, we first propose a framework that describes the process of dealing with a complex scheduling problem. Next, to learn the dispatching pattern, we formally define the scheduling problem through the construction of objective functions and related constraints for the manufacturing tasks. We then apply a reinforcement learning approach that incorporates the real-time production environment to generate optimal policies under various manufacturing-process conditions. When tested under different objectives and constraint conditions, our results demonstrate that the proposed learning-based method provides better performance than most common dispatching rules.