Special Issue on Stochastic Manufacturing Systems
Title | Special Issue on Stochastic Manufacturing Systems PDF eBook |
Author | John A. Buzacott |
Publisher | |
Pages | 206 |
Release | 2008 |
Genre | Production control |
ISBN |
Special Issue on Manufacturing Systems
Title | Special Issue on Manufacturing Systems PDF eBook |
Author | |
Publisher | |
Pages | 102 |
Release | 1993 |
Genre | |
ISBN |
Hierarchical Decision Making in Stochastic Manufacturing Systems
Title | Hierarchical Decision Making in Stochastic Manufacturing Systems PDF eBook |
Author | Suresh P. Sethi |
Publisher | Springer Science & Business Media |
Pages | 420 |
Release | 2012-12-06 |
Genre | Technology & Engineering |
ISBN | 146120285X |
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.
Stochastic Modeling of Manufacturing Systems
Title | Stochastic Modeling of Manufacturing Systems PDF eBook |
Author | George Liberopoulos |
Publisher | Springer Science & Business Media |
Pages | 363 |
Release | 2005-12-12 |
Genre | Business & Economics |
ISBN | 3540290575 |
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.
Control of Manufacturing Systems
Title | Control of Manufacturing Systems PDF eBook |
Author | Sanjay Joshi |
Publisher | |
Pages | 73 |
Release | 1992 |
Genre | |
ISBN |
Stochastic Models of Manufacturing Systems
Title | Stochastic Models of Manufacturing Systems PDF eBook |
Author | John A. Buzacott |
Publisher | Englewood Cliffs, N.J. : Prentice Hall |
Pages | 586 |
Release | 1993 |
Genre | Business & Economics |
ISBN |
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.
Average-Cost Control of Stochastic Manufacturing Systems
Title | Average-Cost Control of Stochastic Manufacturing Systems PDF eBook |
Author | Suresh P. Sethi |
Publisher | Springer Science & Business Media |
Pages | 323 |
Release | 2006-03-22 |
Genre | Business & Economics |
ISBN | 0387276157 |
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.