• 1.1 The Problem Solving Process
• 1.2 Problems, Models and Methods
• 1.3 About the Book
• 1.4 Exercises
• Bibliography

• 2.1 A Manufacturing Example
• 2.2 Computational Considerations
• 2.3 Terminology
• 2.4 Solution Characteristics
• 2.5 Solutions and Sensitivity Analysis
• 2.6 Problem Classes
• 2.7 Exercises
• Bibliography

3 Linear Programming Methods

• 3.1 Standard Form of Model
• 3.2 Preparing the Model
• 3.3 Geometric Properties of a Linear Program
• 3.4 Simplex Tableau
• 3.5 Information from the Simplex Form
• 3.6 Simplex Method Using Tableaus
• 3.7 Special Situations
• 3.8 The Initial Basic Feasible Solution
• 3.9 Dual Simplex Algorithm
• 3.10 Simplex Method Using Matrix Notation
• 3.11 Revised Simplex Method
• 3.12 Exercises
• Bibliography

• 4.1 Sensitivity Analysis
• 4.2 The Dual Linear Program
• 4.3 Interior Point Methods
• 4.4 Exercises
• Bibliography

• 5.1 Classical Models
• 5.2 Extensions of the Basic Models
• 5.3 Linear Programming Model
• 5.4 Minimum Cost Flow Problem
• 5.6 Exercises
• Bibliography

• 6.1 Transportation Problem
• 6.2 Shortest Path Problem
• 6.3 Maximum Flow Problem
• 6.4 Pure Minimum Cost Flow Problem
• 6.5 Exercises
• Bibliography

7 Integer Programming Models

• 7.1 Site Selection Example
• 7.2 General Considerations
• 7.3 System Design with Fixed Charges
• 7.4 Facility Location Problem
• 7.5 Covering and Partitioning Problems
• 7.6 Distance Problems
• 7.7 Examples
• 7.8 Nonlinear Objective Function
• 7.9 Exercises
• Bibliography

• 8.1 Greedy Algorithms
• 8.2 Solution by Enumeration
• 8.3 Branch and Bound
• 8.4 Cutting Plane Methods
• 8.5 Additional Cuts
• 8.6 Exercises
• Bibliography

• 9.1 Manufacturing Example
• 9.2 General Considerations
• 9.3 Determining Convexity
• 9.4 Applications
• 9.5 Problem Classes
• 9.6 Exercises
• Bibliography

• 10.1 Classical Optimization
• 10.2 Equality Constraints
• 10.3 Inequality Constraints
• 10.4 Separable Programming
• 10.5 Quadratic Programming
• 10.6 One-Dimensional Search Methods
• 10.7 Multidimensional Search Methods
• 10.8 Exercises
• Bibliography

• 11.1 Continuous-Time Markov Chains
• 11.2 Realization of the Process
• 11.3 Discrete-Time Markov Chains
• 11.4 Examples of Stochastic Processes
• 11.5 Assessment of Stochastic Models
• 11.6 Exercises
• Bibliography

• 12.1 Transition Matrix
• 12.2 Multi-Step Transitions
• 12.3 Steady-State Solutions
• 12.4 Economic Analysis
• 12.5 Applications
• 12.6 Exercises
• Bibliography

• 13.1 State-Transition Probabilities
• 13.2 Classification of States
• 13.3 Multi-Step Transition Matrix
• 13.4 Transient Probability Vector
• 13.5 Steady-State Probabilities
• 13.6 Economic Analysis
• 13.7 First Passage Times
• 13.8 Absorbing State Probabilities
• 13.9 Exercises
• Bibliography

• 14.1 Markovian Property
• 14.2 Model Components
• 14.3 Transient Solutions
• 14.4 Steady-State Solutions
• 14.5 Design Alternatives for ATM
• 14.6 Birth and Death Processes
• 14.7 Probabilistic Transitions
• 14.8 Exercises
• Bibliography

15 Mathematics of Continuous-Time Markov Chains

• 15.1 Embedded Discrete-Time Markov Chain
• 15.2 Steady-State Probabilities
• 15.3 Economic Analysis
• 15.4 First Passage Times
• 15.5 Birth-Death Processes
• 15.6 Exercises
• Bibliography

• 16.1 System Characteristics
• 16.2 Markov Queueing Systems
• 16.3 Non-Markov Systems
• 16.4 Exercises
• Bibliography

• 17.1 Jackson Networks
• 17.2 Network Examples
• 17.3 Expected Flow Time through Network
• 17.4 Non-Poisson Networks
• 17.5 Optimal Design of Queues
• 17.6 Systems with Travel Times
• 17.7 Exercises
• Bibliography

18 Simulation

• 18.1 Nature and Motivation for Simulation
• 18.2 Model Components
• 18.3 Inverse Transformation Method for Generating Random Variates
• 18.4 Simulating Complex Random Variables
• 18.5 Discrete Event Dynamic Simulation
• 18.6 Simulation of Inventory System
• 18.7 Steps in a Simulation Study
• 18.8 Implementation Issues
• 18.9 Analysis of Simulation Output
• 18.10 Assessment of Methodology
• 18.11 Exercises
• Bibliography