**Introduction to Operations Research 11th INTERNATIONAL Edition by Frederick Hillier, ISBN-13: 978-1260575873**

[PDF eBook eTextbook]

- Publisher: McGraw-Hill; 11th edition (February 9, 2021)
- Language: English
- ISBN-10: 126057587X
- ISBN-13: 978-1260575873

For over four decades, * Introduction to Operations Research* has been the classic text on operations research. While building on the classic strengths of the text, the author continues to find new ways to make the text current and relevant to students. One way is by incorporating a wealth of state-of-the art, user-friendly software and more coverage of business applications than ever before. When the first co-author received the prestigious Expository Writing Award from INFORMS for a recent edition, the award citation described the reasons for the book’s great success as follows:

“Two features account for this success. First, the editions have been outstanding from students’ points of view due to excellent motivation, clear and intuitive explanations, good examples of professional practice, excellent organization of material, very useful supporting software, and appropriate but not excessive mathematics. Second, the editions have been attractive from instructors’ points of view because they repeatedly infuse state-of-the-art material with remarkable lucidity and plain language.”

**Table of Contents:**

Title Page

Copyright Page

About the Authors

About the Case Writers

Dedication

Table of Contents

Supplements Available on the Text Website

Preface

Chapter 1: Introduction

1.1 The Origins of Operations Research

1.2 The Nature of Operations Research

1.3 The Relationship between Analytics and Operations Research

1.4 The Impact of Operations Research

1.5 Some Trends that Should Further Increase the Future Impact of Operations Research

1.6 Algorithms and or Courseware

Selected References

Problems

Chapter 2: Overview of How Operations Research and Analytics Professionals Analyze Problems

2.1 Defining the Problem

2.2 Gathering and Organizing Relevant Data

2.3 Using Descriptive Analytics to Analyze Big Data

2.4 Using Predictive Analytics to Analyze Big Data

2.5 Formulating a Mathematical Model to Begin Applying Prescriptive Analytics

2.6 Learning How to Derive Solutions from the Model

2.7 Testing the Model

2.8 Preparing to Apply the Model

2.9 Implementation

2.10 Conclusions

Selected References

Problems

Chapter 3: Introduction to Linear Programming

3.1 Prototype Example

3.2 The Linear Programming Model

3.3 Assumptions of Linear Programming

3.4 Additional Examples

3.5 Formulating and Solving Linear Programming Models on a Spreadsheet

3.6 Formulating Very Large Linear Programming Models

3.7 Conclusions

Selected References

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Problems

Case 3.1 Reclaiming Solid Wastes

Previews of Added Cases on Our Website

Case 3.2 Cutting Cafeteria Costs

Case 3.3 Staffing a Call Center

Case 3.4 Promoting a Breakfast Cereal

Case 3.5 Auto Assembly

Chapter 4: Solving Linear Programming Problems: The Simplex Method

4.1 The Essence of the Simplex Method

4.2 Setting Up the Simplex Method

4.3 The Algebra of the Simplex Method

4.4 The Simplex Method in Tabular Form

4.5 Tie Breaking in the Simplex Method

4.6 Reformulating Nonstandard Models to Prepare for Applying the Simplex Method

4.7 The Big M Method for Helping to Solve Reformulated Models

4.8 The Two-Phase Method is an Alternative to the Big M Method

4.9 Postoptimality Analysis

4.10 Computer Implementation

4.11 The Interior-Point Approach to Solving Linear Programming Problems

4.12 Conclusions

Appendix 4.1: An Introduction to Using LINDO and LINGO

Selected References

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Problems

Case 4.1 Fabrics and Fall Fashions

Previews of Added Cases on Our Website

Case 4.2 New Frontiers

Case 4.3 Assigning Students to Schools

Chapter 5: The Theory of the Simplex Method

5.1 Foundations of the Simplex Method

5.2 The Simplex Method in Matrix Form

5.3 A Fundamental Insight

5.4 The Revised Simplex Method

5.5 Conclusions

Selected References

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Problems

Chapter 6: Duality Theory

6.1 The Essence of Duality Theory

6.2 Primal-Dual Relationships

6.3 Adapting to Other Primal Forms

6.4 The Role of Duality Theory in Sensitivity Analysis

6.5 Conclusions

Selected References

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Problems

Chapter 7: Linear Programming under Uncertainty

7.1 The Essence of Sensitivity Analysis

7.2 Applying Sensitivity Analysis

7.3 Performing Sensitivity Analysis on a Spreadsheet

7.4 Robust Optimization

7.5 Chance Constraints

7.6 Stochastic Programming with Recourse

7.7 Conclusions

Selected References

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Problems

Case 7.1 Controlling Air Pollution

Previews of Added Cases on Our Website

Case 7.2 Farm Management

Case 7.3 Assigning Students to Schools, Revisited

Case 7.4 Writing a Nontechnical Memo

Chapter 8: Other Algorithms for Linear Programming

8.1 The Dual Simplex Method

8.2 Parametric Linear Programming

8.3 The Upper Bound Technique

8.4 An Interior-Point Algorithm

8.5 Conclusions

Selected References

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Problems

Chapter 9: The Transportation and Assignment Problems

9.1 The Transportation Problem

9.2 A Streamlined Simplex Method for the Transportation Problem

9.3 The Assignment Problem

9.4 A Special Algorithm for the Assignment Problem

9.5 Conclusions

Selected References

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Problems

Case 9.1 Shipping Wood to Market

Previews of Added Cases on Our Website

Case 9.2 Continuation of the Texago Case Study

Case 9.3 Project Pickings

Chapter 10: Network Optimization Models

10.1 Prototype Example

10.2 The Terminology of Networks

10.3 The Shortest-Path Problem

10.4 The Minimum Spanning Tree Problem

10.5 The Maximum Flow Problem

10.6 The Minimum Cost Flow Problem

10.7 The Network Simplex Method

10.8 A Network Model for Optimizing a Project’s Time-Cost Trade-Off

10.9 Conclusions

Selected References

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Problems

Case 10.1 Money in Motion

Previews of Added Cases on Our Website

Case 10.2 Aiding Allies

Case 10.3 Steps to Success

Chapter 11: Dynamic Programming

11.1 A Prototype Example for Dynamic Programming

11.2 Characteristics of Dynamic Programming Problems

11.3 Deterministic Dynamic Programming

11.4 Probabilistic Dynamic Programming

11.5 Conclusions

Selected References

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Problems

Chapter 12: Integer Programming

12.1 Prototype Example

12.2 Some BIP Applications

12.3 Using Binary Variables to Deal with Fixed Charges

12.4 A Binary Representation of General Integer Variables

12.5 Some Perspectives on Solving Integer Programming Problems

12.6 The Branch-and-Bound Technique and its Application to Binary Integer Programming

12.7 A Branch-and-Bound Algorithm for Mixed Integer Programming

12.8 The Branch-and-Cut Approach to Solving BIP Problems

12.9 The Incorporation of Constraint Programming

12.10 Conclusions

Selected References

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Problems

Case 12.1 Capacity Concerns

Previews of Added Cases on Our Website

Case 12.2 Assigning Art

Case 12.3 Stocking Sets

Case 12.4 Assigning Students to Schools, Revisited Again

Chapter 13: Nonlinear Programming

13.1 Sample Applications

13.2 Graphical Illustration of Nonlinear Programming Problems

13.3 Types of Nonlinear Programming Problems

13.4 One-Variable Unconstrained Optimization

13.5 Multivariable Unconstrained Optimization

13.6 The Karush-Kuhn-Tucker (KKT) Conditions for Constrained Optimization

13.7 Quadratic Programming

13.8 Separable Programming

13.9 Convex Programming

13.10 Nonconvex Programming (with Spreadsheets)

13.11 Conclusions

Selected References

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Problems

Case 13.1 Savvy Stock Selection

Previews of Added Cases on Our Website

Case 13.2 International Investments

Case 13.3 Promoting a Breakfast Cereal, Revisited

Chapter 14: Metaheuristics

14.1 The Nature of Metaheuristics

14.2 Tabu Search

14.3 Simulated Annealing

14.4 Genetic Algorithms

14.5 Conclusions

Selected References

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Problems

Chapter 15: Game Theory

15.1 The Formulation of Two-Person, Zero-Sum Games

15.2 Solving Simple Games—A Prototype Example

15.3 Games with Mixed Strategies

15.4 Graphical Solution Procedure

15.5 Solving by Linear Programming

15.6 Extensions

15.7 Conclusions

Selected References

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Problems

Chapter 16: Decision Analysis

16.1 A Prototype Example

16.2 Decision Making without Experimentation

16.3 Decision Making with Experimentation

16.4 Decision Trees

16.5 Utility Theory

16.6 The Practical Application of Decision Analysis

16.7 Multiple Criteria Decision Analysis, Including Goal Programming

16.8 Conclusions

Selected References

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Problems

Case 16.1 Brainy Business

Preview of Added Cases on Our Website

Case 16.2 Smart Steering Support

Case 16.3 Who Wants to Be a Millionaire?

Case 16.4 University Toys and the Engineering Professor Action Figures

Chapter 17: Queueing Theory

17.1 Prototype Example

17.2 Basic Structure of Queueing Models

17.3 Some Common Types of Real Queueing Systems

17.4 The Role of the Exponential Distribution

17.5 The Birth-and-Death Process

17.6 Queueing Models Based on the Birth-and-Death Process

17.7 Queueing Models Involving Nonexponential Distributions

17.8 Priority-Discipline Queueing Models

17.9 Queueing Networks

17.10 The Application of Queueing Theory

17.11 Behavioral Queueing Theory

17.12 Conclusions

Selected References

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Problems

Case 17.1 Reducing In-Process Inventory

Preview of an Added Case on Our Website

Case 17.2 Queueing Quandary

Chapter 18: Inventory Theory

18.1 Examples

18.2 Components of Inventory Models

18.3 Deterministic Continuous-Review Models

18.4 A Deterministic Periodic-Review Model

18.5 Deterministic Multiechelon Inventory Models for Supply Chain Management

18.6 A Stochastic Continuous-Review Model

18.7 A Stochastic Single-Period Model for Perishable Products

18.8 Revenue Management

18.9 Conclusions

Selected References

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Problems

Case 18.1 Brushing Up on Inventory Control

Previews of Added Cases on Our Website

Case 18.2 TNT: Tackling Newsboy’s Teaching

Case 18.3 Jettisoning Surplus Stock

Chapter 19: Markov Decision Processes

19.1 A Prototype Example

19.2 A Model for Markov Decision Processes

19.3 Linear Programming and Optimal Policies

19.4 Markov Decision Processes in Practice

19.5 Conclusions

Selected References

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Problems

Chapter 20: Simulation

20.1 The Essence of Simulation

20.2 Some Common Types of Applications of Simulation

20.3 Generation of Random Numbers

20.4 Generation of Random Observations from a Probability Distribution

20.5 Simulation Optimization

20.6 Outline of a Major Simulation Study

20.7 Conclusions

Selected References

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Problems

Case 20.1 Reducing In-Process Inventory, Revisted

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Case 20.2 Planning Planers

Case 20.3 Pricing under Pressure

Appendix 1. Documentation for the OR Courseware

Appendix 2. Convexity

Appendix 3. Classical Optimization Methods

Appendix 4. Matrices and Matrix Operations

Appendix 5. Table for a Normal Distribution

Partial Answers to Selected Problems

Author Index

Subject Index

Supplements

Chapter 3: Additional Cases

Supplement to Chapter 6

An Economic Interpretation of the Dual Problem and the Simplex Method

Problem

Supplement 1 to Chapter 9

A Case Study with Many Transportation Problems

Supplement 2 to Chapter 9

The Construction of Initial BF Solutions for Transportation Problems

Problems

Supplement to Chapter 12

Some Innovative Uses of Binary Variables in Model Formulation

Problems

Supplement to Chapter 16

Preemptive Goal Programming and Its Solution Procedures

Problems

Case 16S-1 A Cure for Cuba

Case 16S-2 Airport Security

Supplement to Chapter 18

Stochastic Periodic-Review Models

Problems

Supplement 1 to Chapter 19

A Policy Improvement Algorithm for Finding Optimal Policies

Problems

Supplement 2 to Chapter 19

A Discounted Cost Criterion

Problems

Chapter 20: Additional Cases

Supplement 1 to Chapter 20

Variance-Reducing Techniques

Problems

Supplement 2 to Chapter 20

Regenerative Method of Statistical Analysis

Problems

Chapter 21

The Art of Modeling with Spreadsheets

21.1 A Case Study: The Everglade Golden Years Company Cash Flow Problem

21.2 Overview of the Process of Modeling with Spreadsheets

21.3 Some Guidelines for Building “Good” Spreadsheet Models

21.4 Debugging a Spreadsheet Model

21.5 Conclusions

Selected References

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Problems

Case 21.1 Prudent Provisions for Pensions

Chapter 22

Project Management with PERT/CPM

22.1 A Prototype Example—The Reliable Construction Co. Project

22.2 Using a Network to Visually Display a Project

22.3 Scheduling a Project with PERT/CPM

22.4 Dealing with Uncertain Activity Durations

22.5 Considering Time-Cost Trade-Offs

22.6 Scheduling and Controlling Project Costs

22.7 An Evaluation of PERT/CPM

22.8 Conclusions

Selected References

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Problems

Case 22.1 “School’s out forever . . .”

Chapter 23

Additional Special Types of Linear Programming Problems

23.1 The Transshipment Problem

23.2 Multidivisional Problems

23.3 The Decomposition Principle for Multidivisional Problems

23.4 Multitime Period Problems

23.5 Multidivisional Multitime Period Problems

23.6 Conclusions

Selected References

Problems

Chapter 24

Probability Theory

24.1 Sample Space

24.2 Random Variables

24.3 Probability and Probability Distributions

24.4 Conditional Probability and Independent Events

24.5 Discrete Probability Distributions

24.6 Continuous Probability Distributions

24.7 Expectation

24.8 Moments

24.9 Bivariate Probability Distribution

24.10 Marginal and Conditional Probability Distributions

24.11 Expectations for Bivariate Distributions

24.12 Independent Random Variables and Random Samples

24.13 Law of Large Numbers

24.14 Central Limit Theorem

24.15 Functions of Random Variables

Selected References

Problems

Chapter 25

Reliability

25.1 Structure Function of a System

25.2 System Reliability

25.3 Calculation of Exact System Reliability

25.4 Bounds on System Reliability

25.5 Bounds on Reliability Based upon Failure Times

25.6 Conclusions

Selected References

Problems

Chapter 26

The Application of Queueing Theory

26.1 Examples

26.2 Decision Making

26.3 Formulation of Waiting-Cost Functions

26.4 Decision Models

26.5 The Evaluation of Travel Time

26.6 Conclusions

Selected References

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Problems

Chapter 27

Forecasting

27.1 Some Applications of Forecasting

27.2 Judgmental Forecasting Methods

27.3 Time Series

27.4 Forecasting Methods for a Constant-Level Model

27.5 Incorporating Seasonal Effects into Forecasting Methods

27.6 An Exponential Smoothing Method for a Linear Trend Model

27.7 Forecasting Errors

27.8 The ARIMA Method

27.9 Causal Forecasting with Linear Regression

27.10 Conclusions

Selected References

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Problems

Case 27.1 Finagling the Forecasts

Chapter 28

Markov Chains

28.1 Stochastic Processes

28.2 Markov Chains

28.3 Chapman-Kolmogorov Equations

28.4 Classification of States of a Markov Chain

28.5 Long-Run Properties of Markov Chains

28.6 First Passage Times

28.7 Absorbing States

28.8 Continuous Time Markov Chains

Selected References

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Problems

Professor emeritus of operations research at Stanford University. ** Dr. Frederick Hillier** is especially known for his classic, award-winning text, Introduction to Operations Research, co-authored with the late Gerald J. Lieberman, which has been translated into well over a dozen languages and is currently in its 8th edition. The 6th edition won honorable mention for the 1995 Lanchester Prize (best English-language publication of any kind in the field) and Dr. Hillier also was awarded the 2004 INFORMS Expository Writing Award for the 8th edition. His other books include The Evaluation of Risky Interrelated Investments, Queueing Tables and Graphs, Introduction to Stochastic Models in Operations Research, and Introduction to Mathematical Programming. He received his BS in industrial engineering and doctorate specializing in operations research and management science from Stanford University. The winner of many awards in high school and college for writing, mathematics, debate, and music, he ranked first in his undergraduate engineering class and was awarded three national fellowships (National Science Foundation, Tau Beta Pi, and Danforth) for graduate study. Dr. Hillier’s research has extended into a variety of areas, including integer programming, queueing theory and its application, statistical quality control, and production and operations management. He also has won a major prize for research in capital budgeting.

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