Hierarchical Decision Making in Stochastic Manufacturing Systems PDF Download
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Author: Suresh P. Sethi
Publisher: Springer Science & Business Media
ISBN: 146120285X
Category : Technology & Engineering
Languages : en
Pages : 420
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Book Description
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.
Author: Suresh P. Sethi
Publisher: Springer Science & Business Media
ISBN: 146120285X
Category : Technology & Engineering
Languages : en
Pages : 420
Get Book
Book Description
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.
Author: George Yin
Publisher: American Mathematical Soc.
ISBN: 9780821897027
Category : Business & Economics
Languages : en
Pages : 420
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Book Description
In this volume, leading experts in mathematical manufacturing research and related fields review and update recent advances of mathematics in stochastic manufacturing systems and attempt to bridge the gap between theory and applications. The topics covered include scheduling and production planning, modeling of manufacturing systems, hierarchical control for large and complex systems, Markov chains, queueing networks, numerical methods for system approximations, singular perturbed systems, risk-sensitive control, stochastic optimization methods, discrete event systems, and statistical quality control.
Author: Suresh P. Sethi
Publisher: Springer Science & Business Media
ISBN: 0387276157
Category : Business & Economics
Languages : en
Pages : 324
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Book Description
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.
Author: George Yin
Publisher: American Mathematical Soc.
ISBN: 9780821807552
Category : Business & Economics
Languages : en
Pages : 399
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Book Description
In this volume, leading experts in mathematical manufacturing research and related fields review and update recent advances of mathematics in stochastic manufacturing systems and attempt to bridge the gap between theory and applications. The topics covered include scheduling and production planning, modeling of manufacturing systems, hierarchical control for large and complex systems, Markov chains, queueing networks, numerical methods for system approximations, singular perturbed systems, risk-sensitive control, stochastic optimization methods, discrete event systems, and statistical quality control.
Author: John A. Buzacott
Publisher: Englewood Cliffs, N.J. : Prentice Hall
ISBN:
Category : Business & Economics
Languages : en
Pages : 586
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Book Description
Outlining the major issues that have to be addressed in the design and operation of each type of system, this new text explores the stochastic models of a wide range of manufacturing systems. It covers flow lines, job shops, transfer lines, flexible manufacturing systems, flexible assembly systems, cellular systems, and more. For professionals working in the area of manufacturing system modelling.
Author: George Liberopoulos
Publisher: Springer Science & Business Media
ISBN: 3540290575
Category : Business & Economics
Languages : en
Pages : 363
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Book Description
Manufacturing systems rarely perform exactly as expected and predicted. Unexpected events, such as order changes, equipment failures and product defects, affect the performance of the system and complicate decision-making. This volume is devoted to the development of analytical methods aiming at responding to variability in a way that limits its corrupting effects on system performance. The book includes fifteen novel chapters that mostly focus on the development and analysis of performance evaluation models of manufacturing systems using decomposition-based methods, Markovian and queuing analysis, simulation, and inventory control approaches. They are organized into four distinct sections to reflect their shared viewpoints: factory design, unreliable production lines, queuing network models, production planning and assembly.
Author: David D. Yao
Publisher: Springer
ISBN: 9783540943198
Category : Production management
Languages : en
Pages : 360
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Book Description
Author: Suresh P. Sethi
Publisher:
ISBN: 9786610608386
Category : Cost accounting
Languages : en
Pages : 324
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Book Description
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.
Author: J. George Shanthikumar
Publisher: Springer Science & Business Media
ISBN: 9781402075087
Category : Business & Economics
Languages : en
Pages : 436
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Book Description
The Editors have taken the occasion of Professor John A. Buzacott's retirement as a motivating event to develop this volume. The objectives of Stochastic Modeling And Optimization Of Manufacturing Systems And Supply Chains is to both honor John Buzacott's achievements and to publish a set of well-written chapters on highly timely topics in the field of manufacturing and supply chain management. The book is organized into two parts. The first part focuses on aspects of manufacturing systems modeling. This part includes chapters on the evolution of manufacturing systems modeling, queuing network models and related software technologies, two-moment approximation for fork/join queues, and asymptotic optimality of a scheduling policy. (Midwest).
Author: David D. Yao
Publisher: Springer Science & Business Media
ISBN: 1461226708
Category : Business & Economics
Languages : en
Pages : 369
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Book Description
Manufacturing systems have become increasingly complex over recent years. This volume presents a collection of chapters which reflect the recent developments of probabilistic models and methodologies that have either been motivated by manufacturing systems research or been demonstrated to have significant potential in such research. The editor has invited a number of leading experts to present detailed expositions of specific topics. These include: Jackson networks, fluid models, diffusion and strong approximations, the GSMP framework, stochastic convexity and majorization, perturbation analysis, scheduling via Brownian models, and re-entrant lines and dynamic scheduling. Each chapter has been written with graduate students in mind, and several have been used in graduate courses that teach the modeling and analysis of manufacturing systems.
