Stochastic models for the design, coordination, and control of manufacturing systems

摘要

Manufacturing systems rarely perform exactly as expected and predicted. Unexpected events always happen: customers may change their orders, equipment may break down, workers may be absent, raw parts may not arrive on time, processed parts may be defective, etc. Such randomness affects the performance of the system and complicates decision-making. Responding to unexpected disturbances occupies a significant amount of time of manufacturing managers. There are two possible plans of action for addressing randomness: reduce it or respond to it in a way that limits its corrupting effect on system performance. This special issue is devoted to the second. It includes thirteen novel articles on stochastic models for the design, coordination, and control of manufacturing systems. The advantage of modeling is that it can lead to the deepest understanding of the system and give the most practical results, provided that the models apply well to the real systems that they are intended to represent. The articles in this issue focus on the development and analysis of performance evaluation models using decomposition-based methods, Markovian and queuing analysis, simulation, and inventory control approaches. They are organized into four distinct sections to reflect their shared viewpoints.