Online Job Shop Scheduling and Dynamic Predictive Maintenance by Machine Learning

This paper proposes several innovative approaches to model evaluation after obtaining the reinforcement learning model of scheduling with predictive maintenance. To train this model, its reward and loss function must be determined according to the conditions of the workshop environment. One of the innovations of this paper is to provide a definition of the reward function for the issue. This learning model is examined in different modes of work entry into the workshop and the results obtained from other scheduling methods show better outputs. The predictive maintenance model is evaluated by four learning methods and the quality of these models is examined. By selecting and adding the best machine failure model to the scheduling reinforcement learning model, the instant tasks entered into the workshop are assigned to the machines. By comparing the proposed method with the previous ones, the best performance is found and shown.