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: Generative models, which yield a set of decision variables optimizing a performance measure, based on mathematical optimization Evaluative models, which evaluate some performance measures as a function of some predefined decision strategy. Within this class Petri Nets and Queueing Networks are discussed. 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.