Design, Implementation and Evaluation of Reinforcement Learning for an Adaptive Order Dispatching in Job Shop Manufacturing Systems

Modern production systems tend to have smaller batch sizes, a larger product variety and more complex material flow systems. Since a human oftentimes can no longer act in a sufficient manner as a decision maker under these circumstances, the demand for efficient and adaptive control systems is rising. This paper introduces a methodical approach as well as guideline for the design, implementation and evaluation of Reinforcement Learning (RL) algorithms for an adaptive order dispatching. Thereby, it addresses production engineers willing to apply RL. Moreover, a real-world use case shows the successful application of the method and remarkable results supporting real-time decision-making. These findings comprehensively illustrate and extend the knowledge on RL.