Advanced Models for Manufacturing Systems Management
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Summary
This book presents the mathematical models applicable to manufacturing systems management, covering problems from production to real time control. It explores manufacturing systems from the viewpoints of both physical structure and performance measures. Two broad classes of mathematical models are covered in detail:
Advanced Models for Manufacturing Systems Management describes dynamic systems modeling by state equations, a unifying framework for a wide variety of models. The text/reference stresses model building, but it examines model solving as well. Computational techniques are illustrated, such as linear programming, branch and bound methods, and dynamic programming. Particular emphasis is given to the development of heuristic methods from mathematical models.
The book provides readers with valuable tools for management and design. The use of descriptive models within an optimization algorithm is considered. Numerous examples illustrate theoretical concepts throughout text. Appendices are given at the end of the book in order to recall fundamentals, such as linear programming and graph theory. Appendices also appear within each chapter. In this way, readers can follow the main reading path without getting involved with details; these appendices can be read at a later time. This textual structure makes this book particularly well suited for self-study. Advanced Models for Manufacturing Systems Management is beneficial reading for both students and practitioners.
Table of Contents
Manufacturing Systems Modeling
The Nature of Mathematical Models of Manufacturing Systems
Dynamic Models of Manufacturing Systems
An Overview of Management Problems in Manufacturing Systems
Plan of the Book
For Further Reading
Optimization Models and Model Solving
Classes of Optimization Models
An Overview of Optimization Methods
Complexity of Optimization Problems
Good and Bad Model Formulations
Developing Heuristics from Mathematical Models
The Theory of NP-Completeness
An Outlook on Multi-Objective Optimization
For Further Reading
Discrete Time Models
Aggregate Production Planning
The Capacitated Lot-Sizing Problem
The Discrete Lot-Sizing and Scheduling Problem
Continuous Flow Models for Production Scheduling
Discussion: The Flexibility of Discrete Time Models
Strong Formulations for Lot-Sizing Problems
The Single Item DLSP Problem: A Dynamic Programming Approach
For Further Reading
DEDS Models for Scheduling Problems
Classical Machine Scheduling Theory
Classification of Scheduling Problems
Polynomial Complexity Scheduling Problems
Dynamic Programming Approaches
A Modeling Framework Based on Node Potential Assignment
MILP Models for Scheduling Problems
Branch and Bound Methods
Heuristic Scheduling Methods
Discussion
Dynamic Programming for Scheduling a Batch Processor
Periodic Scheduling Problems
A Multi-Objective Approach to Machine Loading
Evaluative Models
Introduction to Queueing Models
Queueing Networks
Computational Methods for Closed Networks
Approximate Analysis of Non-Product Form Queueing Networks
Petri Net Models
Product Forms and Local Balance Equations
Putting Things Together
Integrating Optimization Methods and Evaluative Models
Optimal Control of Failure-Prone Manufacturing Systems
Model Management and Modeling Languages
Appendices
Fundamentals of Mathematical Programming
Linear Programming and Network Optimization
Enumerative and Heuristic Methods for Discrete Optimization
Dynamic Programming
Stochastic Modeling
Problems
Bibliography
Index
Each Chapter Also Includes a "For Further Reading" Section.
