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Stochastic Models of Manufacturing Systems

Stochastic Models of Manufacturing Systems
Author: John A. Buzacott
Publisher: Englewood Cliffs, N.J. : Prentice Hall
Total Pages: 586
Release: 1993
Genre: Business & Economics
ISBN:

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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.


Stochastic Modeling of Manufacturing Systems

Stochastic Modeling of Manufacturing Systems
Author: George Liberopoulos
Publisher: Springer Science & Business Media
Total Pages: 363
Release: 2005-12-12
Genre: Business & Economics
ISBN: 3540290575

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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.


Stochastic Modeling and Analysis of Manufacturing Systems

Stochastic Modeling and Analysis of Manufacturing Systems
Author: David D. Yao
Publisher: Springer Science & Business Media
Total Pages: 369
Release: 2012-12-06
Genre: Business & Economics
ISBN: 1461226708

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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.


Stochastic Modeling and Optimization of Manufacturing Systems and Supply Chains

Stochastic Modeling and Optimization of Manufacturing Systems and Supply Chains
Author: J. George Shanthikumar
Publisher: Springer Science & Business Media
Total Pages: 413
Release: 2012-12-06
Genre: Business & Economics
ISBN: 1461503736

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This volume originates from two workshops, both focusing on themes that are reflected in the title of the volume. The first workshop took place at Eindhoven University of Technology, April 24-26, 2001, on the occasion of the University granting a doctorate honoris causa to Profes sor John A. Buzacott. The second workshop was held on June 15, 2002 at Cornell University (preceding the annual INFORMSjMSOM Confer ence), honoring John's retirement and his lifetime contributions. Each of the two workshops consisted of about a dozen technical presentations. The objective of the volume, however, is not to simply publish the proceedings of the two workshops. Rather, our objective is to put to gether a select set of articles, each organized into a well-written chapter, focusing on a timely topic. Collected into a single volume, these chapters aim to serve as a useful reference for researchers and practitioners alike, and also as reading materials for graduate courses or seminars.


Handbook of Stochastic Models and Analysis of Manufacturing System Operations

Handbook of Stochastic Models and Analysis of Manufacturing System Operations
Author: J. MacGregor Smith
Publisher: Springer Science & Business Media
Total Pages: 397
Release: 2013-05-17
Genre: Business & Economics
ISBN: 1461467772

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This handbook surveys important stochastic problems and models in manufacturing system operations and their stochastic analysis. Using analytical models to design and control manufacturing systems and their operations entail critical stochastic performance analysis as well as integrated optimization models of these systems. Topics deal with the areas of facilities planning, transportation, and material handling systems, logistics and supply chain management, and integrated productivity and quality models covering: • Stochastic modeling and analysis of manufacturing systems • Design, analysis, and optimization of manufacturing systems • Facilities planning, transportation, and material handling systems analysis • Production planning, scheduling systems, management, and control • Analytical approaches to logistics and supply chain management • Integrated productivity and quality models, and their analysis • Literature surveys of issues relevant in manufacturing systems • Case studies of manufacturing system operations and analysis Today’s manufacturing system operations are becoming increasingly complex. Advanced knowledge of best practices for treating these problems is not always well known. The purpose of the book is to create a foundation for the development of stochastic models and their analysis in manufacturing system operations. Given the handbook nature of the volume, introducing basic principles, concepts, and algorithms for treating these problems and their solutions is the main intent of this handbook. Readers unfamiliar with these research areas will be able to find a research foundation for studying these problems and systems.


Hierarchical Decision Making in Stochastic Manufacturing Systems

Hierarchical Decision Making in Stochastic Manufacturing Systems
Author: Suresh P. Sethi
Publisher: Springer Science & Business Media
Total Pages: 420
Release: 2012-12-06
Genre: Technology & Engineering
ISBN: 146120285X

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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.


Performance Analysis of Manufacturing Systems

Performance Analysis of Manufacturing Systems
Author: Tayfur Altiok
Publisher: Springer Science & Business Media
Total Pages: 372
Release: 1997
Genre: Business & Economics
ISBN: 0387947736

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The past two decades have seen a great deal of research into the stochastic modelling of production, manufacturing, and inventory systems for the purpose of improving their performance. This book provides a graduate-level introduction to these techniques covering exact, approximate, and numerical techniques. The author has aimed to strike a balance between theoretical issues and the practical aspects of modelling manufacturing systems. It is based on graduate courses given to operations research and industrial engineering students and includes numerous examples and exercises.