Hierarchical Decision Making in Stochastic Manufacturing Systems (Paperback, Softcover reprint of the original 1st ed. 1994)

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.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Series:	Systems & Control: Foundations & Applications
Release date:	December 2012
First published:	1994
Authors:	Suresh P. Sethi • Qing Zhang
Dimensions:	235 x 155 x 23mm (L x W x T)
Format:	Paperback
Pages:	422
Edition:	Softcover reprint of the original 1st ed. 1994
ISBN-13:	978-1-4612-6694-5
Categories:	Books > Professional & Technical > Technology: general issues > Engineering: general Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematics for scientists & engineers
LSN:	1-4612-6694-7
Barcode:	9781461266945

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