Enhancing Production in Robot-Enabled Manufacturing Systems: A Dynamic Model and Moving Horizon Control Strategy for Mobile Robot Assignment

摘要

Abstract This article presents a dynamic mathematical model for a robot-enabled manufacturing system, where mobile robots independently manage workstation tasks. Each robot possesses one or multiple skills, enabling collaborative work at workstations. A real-time robot assignment problem is formulated to maximize production of the system, and a novel control strategy is developed to address this problem. Leveraging system properties derived from the model and moving window downtime prediction, the problem of maximizing system production is transformed into a more tractable control problem focused on identifying and achieving ideal clean configurations. The proposed solution significantly outperforms various benchmarks, including a pure reinforcement learning-based strategy, underscoring the importance of system understanding and its crucial role in enhancing flexibility and productivity in manufacturing systems.