Modeling and Dynamic Assignment of the Adaptive Buffer Spaces in Serial Production Lines

Abstract

Abstract In production systems, the buffer capacities have usually been assumed to be fixed during normal operations. Inspired by the observations from the real industrial operations, a novel concept of Adaptive Buffer Space (ABS) is proposed in this paper. The ABS is a type of equipment, such as movable racks or mobile robots with racks, which can be used to provide extra storage space for a production line to temporarily increase certain buffers’ capacities in a real-time fashion. A good strategy to assign and reassign the ABS can significantly improve real-time production throughput. In order to model the production systems with changing buffer capacities, a data-driven model is developed to incorporate the impact of buffer capacity variation in system dynamics. Based on the model, a real-time ABS assignment strategy is developed by analyzing real-time buffer levels and machine status. The strategy is demonstrated to be effective in improving the system throughput. An approximate dynamic programming algorithm, referred to as ABS-ADP, is developed to obtain the optimal ABS assignment policy based on the strategy. Traditional ADP algorithms often initialize the state values with zeros or random numbers. In this paper, a knowledge-guided value function initialization method is proposed in ABS-ADP algorithm to expedite the convergence, which saves up to 80% computation time in the case study.