Applying reinforcement learning to plan manufacturing material handling

Abstract

Applying machine learning methods to improve the efficiency of complex manufacturing processes, such as material handling, can be challenging. The interconnectedness of the multiple components that make up real-world manufacturing processes and the typically very large number of variables required to specify procedures and plans within them combine to make it very difficult to map the details of such processes to a formal mathematical representation suitable for conventional optimization methods. Instead, in this work reinforcement learning was applied to produce increasingly efficient plans for material handling in representative manufacturing facilities. Doing so included defining a formal representation of a realistically complex material handling plan, specifying a set of suitable plan change operators as reinforcement learning actions, implementing a simulation-based multi-objective reward function that considers multiple components of material handling costs, and abstracting the many possible material handling plans into a state set small enough to enable reinforcement learning. Experimentation with multiple material handling plans on two separate factory layouts indicated that reinforcement learning could consistently reduce the cost of material handling. This work demonstrates the applicability of reinforcement learning with a multi-objective reward function to realistically complex material handling processes.