Control Policy Optimization for Bernoulli Serial Production Lines With Dynamic Helper Allocation

With the rapid development of Industry 4.0 technologies, the application of artificial intelligence is a more critical task than ever to inject intelligence into the smart manufacturing practice. To satisfy the demand of modern production, developing a real-time automated decision-making system with control and optimization is essential. Due to the advantage of reinforcement learning, it becomes a practical tool for finding complicated control policies as well as for solving optimization problems for production system. In this paper, we concentrate on the control and optimization of production system operations to improve the efficiency and productivity of the manufacturing process. Specifically, we consider serial production lines with finite buffers and machines characterized by the Bernoulli reliability model. Additionally, we assume that multiple shared workforce units can be allocated dynamically during production via a real-time production bottleneck identification and mitigation mechanism in order to increase throughput. Firstly, we discuss the solution to this problem in systems with three and four machines, where two shared workforce units are being assigned during the production. The optimal control policy is derived byQ-learningalgorithm. Then, the study is extended to longer lines with multiple shared workforce units. An aggregation-based hierarchical decomposition approach is proposed to search for the optimized control policy through decomposing the serial line and then construct control policies from bottom up. Numerical experiments are carried out to demonstrate the performance of the proposed approach.