This book articulates a new theory that shows that hierarchical decision making in manufacturing systems can lead to a near optimization of system goals. It will appeal to graduate students and researchers in applied mathematics, operations management, operations research, and systems and control theory.

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This book is concerned with hierarchical control of manufacturing systems under uncertainty. It focuses on system performance measured in long-run average cost criteria, exploring the relationship between control problems with a discounted cost and that with a long-run average cost in connection with hierarchical control. A new theory is articulated that shows that hierarchical decision making in the context of a goal-seeking manufacturing system can lead to a near optimization of its objective. The approach in the book considers manufacturing systems in which events occur at different time scales.

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