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Operations Research: An Introduction, 8th Edition

By Hamdy A. Taha

ISBN-10: 0-13-188923-0

ISBN-13: 978-0-13-188923-1What's this?

Published by Prentice Hall

Pub. Date: Apr 4, 2006

Format: Cloth

Table of Contents

Chapter 1:  What is Operations Research?

1.1  Operations Research Models

1.2  Solving the OR Model

1.3  Queueing and Simulation Models

1.4  Art of Modeling

1.5  More than Just Mathematics

1.6  Phases of an OR Study

1.7  About this Book

     Problems

     References

 

 

Chapter 2:  Modeling with Linear Programming

2.1  Two-Variable LP Model

2.2  Graphical LP Solution

2.3  Selected LP Applications

2.4  Computer Solution with Solver and AMPL

     Problems

     References

 

 

Chapter 3:  The Simplex Method and Sensitivity Analysis

3.1  LP Model in Equation Form

3.2  Transition from Graphical to Algebraic Solution

3.3  The Simplex Method

3.4  Artificial Starting Solution

3.5  Special Cases in the Simplex Method

3.6  Sensitivity Analysis

     Problems

     References

 

 

Chapter 4:  Duality and Post-Optimal Analysis

4.1  Definition of the Dual Problem

4.2  Primal-Dual Relationships

4.3  Economic Interpretation of Duality

4.4  Additional Simplex Algorithms

4.5  Post-Optimal Analysis

     Problems

     References

 

 

Chapter 5:  Transportation Model and its Variants

5.1 Definition of the Transportation Model

5.2  Nontraditional Transportation Models

5.3  The Transportation Algorithm

5.4  The Assignment Model

5.5 The Transshipment Model

     Problems

     References

 

 

Chapter 6:  Network Models

6.1 Scope and Definition of Network Models

6.2 Minimal Spanning Tree Algorithm

6.3 Shortest-Route Problem

6.4  Maximal Flow Model

6.5  CPM and PERT

     Problems

     References

 

 

Chapter 7:  Advanced Linear Programming

7.1  Simplex Method Fundamentals

7.2  Revised Simplex Method

7.3  Bounded Variables Algorithm

7.4  Duality

7.5  Parametric Linear Programming

     Problems

     References

 

 

Chapter 8:  Goal Programming

8.1  A Goal Programming Formulation

8.2  Goal Programming Algorithms 

     Problems

     References

 

 

Chapter 9: Integer Linear Programming

9.1 Illustrative Applications

9.2 Integer Programming Algorithms

9.3 Traveling Salesperson (TSP) Problem

     Problems

     References

 

 

Chapter 10:  Deterministic Dynamic Programming

10.1 Recursive Nature of Computations in DP

10.2 Forward and Backward Recursion

10.3 Selected DP Applications

10.4 Problem of Dimensionality

     Problems

     References

 

 

Chapter 11: Deterministic Inventory Models

11.1 General Inventory Model

11.2 Role of Demand in the Development of Inventory Models

11.3 Static Economic-Order-Quantity (EOQ) Models

11.4 Dynamic EOQ Models

     Problems

     References

 

 

Chapter 12:  Review of Basic Probability

12.1 Laws of Probability

12.2 Random Variables and Probability Distributions

12.3 Expectation of a Random Variable 

12.4 Four Common Probability Distributions

12.5 Empirical Distributions

     Problems

     References

 

Chapter 13: Decision Analysis and Games

13.1 Decision Making under Certainty–Analytic Hierarchy   Process (AHP)

13.2 Decision Making under Risk

13.3 Decision under Uncertainty

13.4 Game Theory

     Problems

     References

 

 

Chapter 14: Probabilistic Inventory Models

14.1 Continuous Review Models

14.2 Single-Period Models

14.3 Multiperiod Model

     Problems

     References

 

 

Chapter 15: Queueing Systems

15.1 Why Study Queues?

15.2 Elements of a Queuing Model

15.3 Role of Exponential Distribution

15.4 Pure Birth and Death Models (Relationship between the     Exponential and Poisson Distributions)

15.5 Generalized Poisson Queuing Model

15.6 Specialized Poisson Queues

15.7 (M/G/1):(GD/Inf/Inf)–Pollaczek-Khintchine (P-K) Formula

15.8 Other Queuing Models

15.9 Queueing Decision Models

     Problems

     References

 

 

Chapter 16: Simulation Modeling

16.1 Monte Carlo Simulation

16.2 Types of Simulation

16.3 Elements of Discrete-Event Simulation

16.4 Generation of Random Numbers

16.5 Mechanics of Discrete Simulation

16.6 Methods for Gathering Statistical Observations

16.7 Simulation Languages

     Problems

     References

 

 

Chapter 17: Markov Chains

17.1 Definition of a Markov Chain

17.2 Absolute and n-Step Transition Probabilities

17.3 Classification of the States in a Markov Chain

17.4Steady-State Probabilities and Mean Return Times of Ergodic Chains

17.5 First Passage Time

17.6 Analysis of Absorbing States

     Problems

     References

 

 

Chapter 18:  Classical Optimization Theory

18.1 Unconstrained Problems

18.2 Constrained Problems

     Problems

     References

 

 

Chapter 19:  Nonlinear Programming Algorithms

19.1 Unconstrained Algorithms

19.2 Constrained Algorithms

     Problems

     References

 

 

Appendix A:  AMPL Modeling Language

A.1 Rudimentary AMPL Model

A.2 Components of AMPL Model

A.3 Mathematical Expressions and Computed Parameters

A.4 Subsets and Indexed Sets

A.5 Accessing External Files

A.6 Interactive Commands

A.7 Iterative and Conditional Execution of AMPL Commads

A.8  Sensitivity Analysis Using AMPL

     Reference

 

Appendix B: Statistical Tables

 

Appendix C: Partial Answers to Selected Problems

 

Index

 

 

On the CD

 

Chapter 20: Additional Network and LP Algorithms

20.1 Minimim-Cost Capacitated Flow Problem

20.2 Decomposition Alogrithm

20.3 Karmarkar Interior-Point Method

     Problems

     References

 

 

Chapter 21:  Forecasting Models

21.1 Moving Average Technique

21.2 Exponential Smoothing

21.3 Maximization of the Event of Achieving a Goal

     Problems

     References

 

 

Chapter 22:  Probabilistic Dynamic Programming

22.1 A Game of Chance

22.2 Investment Problem

22.3 Maximization of the Event of Achieving a Goal

     Problems

     References

 

 

Chapter 23:  Markovian Decision Process

23.1 Scope of the Markovian Decision Problem

23.2 Finite-Stage Dynamic Programming Model

23.3 Infinite-Stage Model

23.4 Linear Programming Solution

     Problems

     References

 

 

Chapter 24:  Case Analysis

Case 1:  Airline Fuel Allocation Using Optimum Tankering

Case 2:  Optimization of Heart Valves Production

Case 3:  Scheduling Appointments at Australian Tourist Commission Trade Events

Case 4:  Saving Federal Travel Dollars

Case 5:  Optimal Ship Routing and Personnel Assignments for Naval Recruitment in Thailand

Case 6:  Allocation of Operating Room Time in Mount Sinai Hospital

Case 7:  Optimizing Trailer Payloads at PFG Building Glass

Case 8:  Optimization of Crosscutting and Log Allocation at Weyerhaeuser

Case 9:  Layout Planning of a Computer Integrated Manufacturing (CIM) Facility

Case 10: Booking Limits in Hotel Reservations

Case 11: Casey’s Problem: Interpreting and Evaluating a New Test

Case 12: Ordering Golfers on the Final Day of Ryder Cup Matches

Case 13: Inventory Decisions in Dell’s Supply Chain

Case 14: Analysis of an Internal Transport System in a Manufacturing Plant

Case 15: Telephone Sales Manpower Planning at Qantas Airways

 

Appendix D: Review of Vectors and Matrices

D.1  Vectors

D.2  Matrices

D.3  Quadratic Forms

D.4  Convex and Concave Functions

     Problems

     References

 

Appendix E: Case Studies

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