Average-Cost Control of Stochastic Manufacturing Systems

Sethi, Suresh P.; Zhang, Qing; Zhang, Han-Qin

ISBN 10: 0387219471 / ISBN 13: 9780387219479

Published by Springer-Verlag New York Inc., 2005

About this Item

This book articulates a new theory that shows that hierarchical decision making can in fact lead to a near optimization of system goals. The material in the book cuts across disciplines. It will appeal to graduate students and researchers in applied mathematics, operations management, operations research, and system and control theory. Series: Stochastic Modelling and Applied Probability. Num Pages: 340 pages, biography. BIC Classification: KJM; PBW; TGB. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 234 x 156 x 20. Weight in Grams: 1440. . 2005. Hardback. . . . . Books ship from the US and Ireland.

Seller Inventory # V9780387219479

Bibliographic Details

• Title: Average-Cost Control of Stochastic Manufacturing Systems
• Publisher: Springer-Verlag New York Inc.
• Publication Date: 2005
• Language: English
• ISBN 10: 0387219471
• ISBN 13: 9780387219479
• Binding: Hardcover
• Condition: New

About this title

Synopsis

Most manufacturing systems are large, complex, and operate in an environment of uncertainty. It is common practice to manage such systems in a hierarchical fashion. This book articulates a new theory that shows that hierarchical decision making can in fact lead to a near optimization of system goals. The material in the book cuts across disciplines. It will appeal to graduate students and researchers in applied mathematics, operations management, operations research, and system and control theory.

From the Back Cover

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.