Mathematical Model of the Assembly Robot Performance in Automated Manufacturing

Robot performance in automated manufacturing supports the competitive edge in manufacturing industries, enhancing customer sustainability and reducing operational costs. The robot cell controller must therefore optimize the operational tasks in the assembly of components or parts of the product to support automated manufacturing strategies for the manufacturing plant. This study proposes a Markov decision process model for robot performance in a steel assembly plant. The performance of the robot is characterized as a Markov chain, and its operational cost matrix represents the expected reward for the Markov decision process problem. This study utilized hourly data for two consecutive weeks. The principal challenge addressed focused on determining the best product assembly option to reduce the assembly expenses of the robot cell. We considered a multi-period planning horizon, where the optimal decision was determined for assembling or not assembling additional products based on the demand and availability of finished assembled products. The model was tested, and the results demonstrated the existence of an optimal state-dependent decision and assembly expenditure of managing the robot cell of the steel assembly plant. As a cost optimization strategy for managing robot cells, improved efficiency and resource utilization for assembled products were realized, supporting automated manufacturing initiatives of the steel assembly plant used in the case study.