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
Learning Aids for this Chapter on Our Website
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
Learning Aids for this Chapter on Our Website
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
Learning Aids for this Chapter on Our Website
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
Previews of Added Cases on Our Website
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
Learning Aids for this Chapter on Our Website
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
Learning Aids for this Chapter on Our Website
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
Learning Aids for this Chapter on Our Website
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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