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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Suresh P. Sethi is the Eugene McDermott Chair Professor of Operations Management and Director of the Center for Intelligent Supply Networks (C4ISN) at the University of Texas at Dallas, USA. He has made significant contributions in the fields of manufacturing and operations management, finance and economics, marketing, industrial engineering, operations research, and optimal control. He is best known for his textbook on optimal control, developments of the Sethi advertising model and Sethi-Skiba points, pioneering works on stochastic inventory models especially with incomplete information, and seminal papers on consumption-investment problems with bankruptcy. He has received numerous prestigious honors and awards such as IEEE Fellow, INFORMS Fellow, SIAM Fellow, POMS Fellow, AAAS Fellow, IITB Distinguished Alum, Tepper School of Business-Alumni Achievement Award, and POMS President (2012). Two conferences have been organized in his honor: in Aix en Provence in 2005 and at the UT Dallas in 2006 with Harry M. Markowitz, a 1990 Nobel Laureate in Economics, as the keynote speaker. Also, two books have been edited in his honor. His past and present editorial positions include Departmental Editor of Production and Operations Management, Corresponding Editor of SIAM Journal on Control and Optimization, and Associate Editor of Operations Research, M&SOM, and Automatica.
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Taschenbuch. Zustand: 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. Artikel-Nr. 9781461266945
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Taschenbuch. Zustand: Neu. Hierarchical Decision Making in Stochastic Manufacturing Systems | Suresh P. Sethi (u. a.) | Taschenbuch | Systems & Control: Foundations & Applications | xvi | Englisch | 2012 | Birkhäuser | EAN 9781461266945 | 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. Artikel-Nr. 106118831
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