Average-Cost Control of Stochastic Manufacturing Systems

1. Auflage. Softcover version of original hardcover Auflage 2005
ISBN: 978-1-4419-1954-0
Verlag: Springer

Buch, Englisch, Band 54, 324 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 517 g

Reihe: Stochastic Modelling and Applied Probability

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.

Zielgruppe

Research

Autoren/Hrsg.

Sethi, Suresh P.

Zhang, Qing

Zhang, Han-Qin

Fachgebiete

• Mathematik | Informatik Mathematik Operations Research
• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Ökonometrie
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensfinanzen Betriebliches Rechnungswesen
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Kybernetik, Systemtheorie, Komplexe Systeme
• Mathematik | Informatik Mathematik Mathematik Interdisziplinär Systemtheorie
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle

Inhaltsverzeichnis

and Models of Manufacturing Systems.- Concept of Near—Optimal Control.- Models of Manufacturing Systems.- Optimal Control of Manufacturing Systems: Existence and Characterization.- Optimal Control of Parallel—Machine Systems.- Optimal Control of Dynamic Flowshops.- Optimal Controls of Dynamic Jobshops.- Risk-Sensitive Control.- Near—Optimal Controls.- Near—Optimal Control of Parallel—Machine Systems.- Near—Optimal Control of Dynamic Flowshops.- Near—Optimal Controls of Dynamic Jobshops.- Near—Optimal Risk—Sensitive Control.- Conclusions.- Further Extensions and Open Research Problems.