9780817637354 - Hierarchical Decision Making in Stochastic Manufacturing Systems (Systems & Control: Foundations & Applications) de Zhang, Qing; Sethi, Suresh P. (15 resultados)

Condición: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. Clean from markings. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,900grams, ISBN:0817637354.

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Condición: New. This text presents an approach to manufacturing systems that is grounded in ideas from economics and control theory. It cuts across the disciplines of operations management, operations research, system and control theory, industrial engineering, probability and statistics and applied maths. Editor(s): Kostant, A. Series: Systems & Control: Foundations and Applications. Num Pages: 438 pages, biography. BIC Classification: PBWL; TBJ; TJFM. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 234 x 156 x 25. Weight in Grams: 796. . 1994. 1994th Edition. hardcover. . . . .

Condición: New. This text presents an approach to manufacturing systems that is grounded in ideas from economics and control theory. It cuts across the disciplines of operations management, operations research, system and control theory, industrial engineering, probability and statistics and applied maths. Editor(s): Kostant, A. Series: Systems & Control: Foundations and Applications. Num Pages: 438 pages, biography. BIC Classification: PBWL; TBJ; TJFM. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 234 x 156 x 25. Weight in Grams: 796. . 1994. 1994th Edition. hardcover. . . . . Books ship from the US and Ireland.

Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - One of the most important methods in dealing with the optimization of large, complex systems is that of hierarchical decomposition. The idea is to reduce the overall complex problem into manageable approximate problems or subproblems, to solve these problems, and to construct a solution of the original problem from the solutions of these simpler prob lems. Development of such approaches for large complex systems has been identified as a particularly fruitful area by the Committee on the Next Decade in Operations Research (1988) [42] as well as by the Panel on Future Directions in Control Theory (1988) [65]. Most manufacturing firms are complex systems characterized by sev eral decision subsystems, such as finance, personnel, marketing, and op erations. They may have several plants and warehouses and a wide variety of machines and equipment devoted to producing a large number of different products. Moreover, they are subject to deterministic as well as stochastic discrete events, such as purchasing new equipment, hiring and layoff of personnel, and machine setups, failures, and repairs.

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Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. One of the most important methods in dealing with the optimization of large, complex systems is that of hierarchical decomposition. The idea is to reduce the overall complex problem into manageable approximate problems or subproblems, to solve these problem.

Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -One of the most important methods in dealing with the optimization of large, complex systems is that of hierarchical decomposition. The idea is to reduce the overall complex problem into manageable approximate problems or subproblems, to solve these problems, and to construct a solution of the original problem from the solutions of these simpler prob lems. Development of such approaches for large complex systems has been identified as a particularly fruitful area by the Committee on the Next Decade in Operations Research (1988) [42] as well as by the Panel on Future Directions in Control Theory (1988) [65]. Most manufacturing firms are complex systems characterized by sev eral decision subsystems, such as finance, personnel, marketing, and op erations. They may have several plants and warehouses and a wide variety of machines and equipment devoted to producing a large number of different products. Moreover, they are subject to deterministic as well as stochastic discrete events, such as purchasing new equipment, hiring and layoff of personnel, and machine setups, failures, and repairs. 444 pp. Englisch.

Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -One of the most important methods in dealing with the optimization of large, complex systems is that of hierarchical decomposition. The idea is to reduce the overall complex problem into manageable approximate problems or subproblems, to solve these problems, and to construct a solution of the original problem from the solutions of these simpler prob lems. Development of such approaches for large complex systems has been identified as a particularly fruitful area by the Committee on the Next Decade in Operations Research (1988) [42] as well as by the Panel on Future Directions in Control Theory (1988) [65]. Most manufacturing firms are complex systems characterized by sev eral decision subsystems, such as finance, personnel, marketing, and op erations. They may have several plants and warehouses and a wide variety of machines and equipment devoted to producing a large number of different products. Moreover, they are subject to deterministic as well as stochastic discrete events, such as purchasing new equipment, hiring and layoff of personnel, and machine setups, failures, and repairs.Springer Basel AG in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 444 pp. Englisch.

Buch. Condición: Neu. Hierarchical Decision Making in Stochastic Manufacturing Systems | Qing Zhang (u. a.) | Buch | xvi | Englisch | 1994 | Birkhäuser Boston | EAN 9780817637354 | Verantwortliche Person für die EU: Springer Basel AG in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.