Development of a Reinforcement Learning-Based Adaptive Scheduling Algorithm for Block Assembly Production Line

Rule-based heuristic algorithms and meta-heuristic algorithms have been studied to solve the scheduling problems of production systems. In recent research, reinforcement learning-based adaptive scheduling algorithms have been applied to solve complex problems with high-dimensional and vast state space. A production system in shipyards is a high-variable system, in which various production factors such as space, workforce, and resources are related. Thus, adaptive scheduling according to the changes in the production system and surrounding environment must be performed in shipyards. In this study, a basic reinforcement learning model for scheduling problems of shipyards was developed. A simplified model of the panel block shop in shipyards was assumed and the optimal policy for determining the input sequence of blocks was learned to reduce the flow time. The open source-based discrete event simulation (DES) kernel SimPy was incorporated into the environment of the reinforcement learning model.