Introduction to Management Science and Business Analytics 7th INTERNATIONAL Edition, ISBN-13: 978-1265040055
[PDF eBook eTextbook]
- The content of the International Edition is the same as other formats.
- Publisher: McGraw-Hill Education (January 1, 2022)
- Language: English
- 768 pages
- ISBN-10: 1265040052
- ISBN-13: 978-1265040055
Hiller’s Management Science and Business Analytics: A Modeling and Case Studies Approach with Spreadsheets, 7e in its newest edition introduces both Management Science and Business Analytics in chapter 1 emphasizing the close relationship between them. The new Chapter 2 is an overview of how MS and BA professionals analyze problems and their similarities, chapter 3 is a new chapter on the role of data mining, clustering classification prediction methodology, some algorithms for implementing this methodology, a powerful SW package for data mining and more. Chapter 4 will continue to focus on key techniques of Business Analytics to complete part 1 of the text and the final chapters are focused on Prescriptive and Predictive Analytics based on models based on Linear programming models, Other Certainty Models, and Uncertainty Models.
Table of Contents:
Table of Contents and Preface
Halftitle
The McGraw Hill Series in Operations and Decision Sciences
Title Page
Copyright Page
Dedication
About the Authors
About the Case Writers
Preface
ADDING BUSINESS ANALYTICS AS A COMPLEMENT TO MANAGEMENT SCIENCE
SPREADSHEETS
A MODELING APPROACH
A CASE STUDIES APPROACH
OTHER SPECIAL FEATURES
A SPECIAL SOFTWARE PACKAGE
A CONTINUING FOCUS ON EXCEL AND ITS SOLVER
NEW FEATURES IN THIS EDITION
REFLECTING THE DIVERSE WORLD AROUND US
THIS EDITION ALSO INCLUDES A SPECIAL TEACHING AND LEARNING PLATFORM
SMARTBOOK®
PROCTORIO REMOTE PROCTORING & BROWSER-LOCKING CAPABILITIES
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OLC-ALIGNED COURSES IMPLEMENTING HIGH-QUALITY ONLINE INSTRUCTION AND ASSESSMENT THROUGH PRECONFIGURED COURSEWARE
TEGRITY: LECTURES 24/7
TEST BUILDER IN CONNECT
WRITING ASSIGNMENT
CREATE YOUR BOOK, YOUR WAY
INSTRUCTOR RESOURCES
STUDENT RESOURCES
AN INVITATION
Acknowledgments
Acknowledgments
Connect
Instructors: Student Success Starts with You
Students: Get Learning that Fits You
Brief Contents
SUPPLEMENTS available on the text website www.mhhe.com/Hillier7e
Contents
SUPPLEMENTS available on the text website www.mhhe.com/Hillier7e
CHAPTERS available at www.mhhe.com/Hillier7e
Chapter 1: Introduction
Introduction
1.1 The Nature of Management Science
Management Science Is a Discipline
Management Science Aids Managerial Decision Making
Management Science Uses a Scientific Approach
Management Science Considers Quantitative Factors
1.2 What is Business Analytics?
The Three Categories of Business Analytics
The Role of Data Science
The Role of Machine Learning
The Role of Artificial Intelligence
1.3 The Relationship Between Management Science and Business Analytics
The Increasing Demand for Business Analytics and Management Science Professionals
1.4 A Case Study: The VRX Company Advertising Budget Problem
Performing Descriptive Analytics to Explore the Data and Better Visualize the Impact of Advertising on Sales
Performing Predictive Analytics to Predict the Impact of Advertising on Sales
Performing Prescriptive Analytics to Determine the Best Advertising Level
1.5 The Impact of Management Science and Business Analytics
1.6 Some Special Features of This Book
1.7 Summary
Glossary
Learning Aids for This Chapter
Solved Problems
1.S1Choosing an Advertising Budget
Problems
Case 1-1: VRX Revisited: Updating the Model with New Data Over Time
VRX Revisited: Updating the Model with New Data Over Time
Chapter 2: Overview of the Analysis Process
Introduction
2.1 A Case Study: First Bank Evaluates Applications for Unsecured Loans
2.2 Define the Problem
The Complementary Roles of the Study Team and Management
Additional Problem Definition Needed for Prescriptive Analytics
Returning to the Case Study: Defining the Problem at First Bank
2.3 Performing Descriptive Analytics
Returning to the Case Study: Gather and Organize the Relevant Data for First Bank
Some Terminology for Descriptive Analytics
Returning to the Case Study: Cleaning the Data at First Bank
Using Analytic Solver to Explore the Data
Returning to the Case Study: Explore the Data at First Bank
Explore the Data with Summary Statistics for First Bank
Explore the Data with Sorting and Filtering at First Bank
Explore the Data Visually at First Bank
2.4 Performing Predictive Analytics
Prediction and Classification Models
Some Terminology for Prediction and Classification Models
Returning to the Case Study: Developing the Model at First Bank
Choosing the Variables to Include in the Model at First Bank
Choosing the Algorithm for the Model at First Bank
Overfitting the Data
Partition the Data
Returning to the Case Study: Partition the Data at First Bank
Returning to the Case Study: Testing the KNN Model at First Bank
The Role of Lift Charts in Assessing the Effectiveness of an Algorithm
Refining the KNN Model at First Bank
Returning to the Case Study: Exploring Several Models and Choosing the Best
Returning to the Case Study: Implementing the Model at First Bank
2.5 Performing Prescriptive Analytics
Returning to the Case Study: Formulating a Decision Model for First Bank
2.6 Summary
Glossary
Learning Aids for This Chapter
Solved Problem
2.S1.Category D Loan Applicants at First Bank
Problems
Case 2-1: Vacations at Vegas Villas
Vacations at Vegas Villas
Chapter 3: Classification and Prediction Models for Predictive Analytics
Introduction
3.1 A Case Study: The Evergreen Solar Predictive Analytics Problem
3.2 Models Based on the k-Nearest-Neighbors (Knn) Algorithm
Summary of the KNN Algorithm
Finding the Nearest Neighbors for the Case Study
The Importance of Rescaling the Data Appropriately
Choosing k = Number of Historical Records to Include in the Prediction
Predicting Numerical Outcomes with the KNN Algorithm
Using Analytic Solver to Apply the KNN Algorithm to the Case Study
Pros and Cons of the KNN Algorithm
3.3 Models Based On Classification Tree And Regression Tree Algorithms
Introduction to the Classification Tree Algorithm
Splitting the Historical Records into Regions for the Case Study
Measuring Homogeneity with the Gini Index
Classification Trees
Further Splitting of the Data Set
Avoid Overfitting the Data
The Final Classification Tree for the Case Study
Executing the Matching Step
Executing the Classification Step
Making Numerical Predictions with Regression Trees
Using Analytic Solver to Apply These Algorithms to the Case Study
Using Analytic Solver Data Mining to Apply the Regression Tree Algorithm
Pros and Cons of Classification Tree and Regression Tree Algorithms
3.4 Other Models Based on Algorithms for Classification and Prediction
The Naïve Bayes Algorithm
Complete Bayes (Direct Method)
A Major Flaw with Complete Bayes
Naïve Bayes
Multiple Linear Regression
Logistic Regression
Choosing the Best Algorithm
3.5 Revisiting the Case Study: Refining and Testing the Models
Apply Data Partitioning to Refine the KNN Model by Choosing the Best Value for k
Apply Data Partitioning to Prune the Classification Tree
Applying Data Partitioning to Test the Various Models
3.6 Affinity Analysis and Recommendation Systems
3.7 Summary
Glossary
Learning Aids for This Chapter
Solved Problems
3.S1.Applying Classification Trees to First Bank
3.S2.Comparing Classification Models for Evergreen Solar
3.S3.Comparing Prediction Models for Evergreen Solar
Problems
Case 3-1: Evergreen Solar Decides to Use Naïve Bayes
Evergreen Solar Decides to Use Naïve Bayes
Case 3-2: Revisiting Vacations at Vegas Villas
Revisiting Vacations at Vegas Villas
Chapter 4: Predictive Analytics Based on Traditional Forecasting Methods
Introduction
4.1 An Overview of the Techniques of Time Series Forecasting
A Forecasting Problem
Some Forecasting Techniques
4.2 A Case Study: The Computer Club Warehouse (CCW) Problem
The CCW Call Center
The Call Center Manager, Lydia Weigelt
Lydia’s Current Forecasting Method
The Plan to Find a Better Forecasting Method
4.3 Applying Time Series Forecasting Methods to the Case Study
Considering Seasonal Effects
The Seasonally Adjusted Time Series
The Last-Value Forecasting Method
The Averaging Forecasting Method
The Moving-Average Forecasting Method
The Exponential Smoothing Forecasting Method
Exponential Smoothing with Trend
The Meeting with the Marketing Manager
Forecasting Software
4.4 The Time Series Forecasting Methods in Perspective
The Goal of the Forecasting Methods
Problems Caused by Shifting Distributions
Comparison of the Forecasting Methods
The Consultant’s Recommendations
4.5 Causal Forecasting with Linear Regression
Causal Forecasting
Linear Regression
The CCW Case Study a Year Later
4.6 Judgmental Forecasting Methods
4.7 Summary
Glossary
Summary of Key Formulas
Learning Aids for This Chapter
Solved Problem
4.S1.Forecasting Charitable Donations at the Union Mission
Problems
Case 4-1: Finagling the Forecasts
Finagling the Forecasts
Chapter 5: Linear Programming: Basic Concepts
Introduction
5.1 A Case Study: The Wyndor Glass Co. Product-Mix Problem
Background
Management’s Discussion of the Issues
The Analytics Department Begins Its Work
5.2 Formulating the Wyndor Problem on a Spreadsheet
Formulating a Spreadsheet Model for the Wyndor Problem
This Spreadsheet Model Is a Linear Programming Model
Characteristics of a Linear Programming Model on a Spreadsheet
Summary of the Formulation Procedure
5.3 The Mathematical Model in the Spreadsheet
Formulating the Wyndor Model Algebraically
Algebraic Model
Terminology for Linear Programming Models
Assumptions of Linear Programming Models
Comparisons of Algebraic Models and Spreadsheet Models
5.4 The Graphical Method for Solving Two-Variable Problems
Summary of the Graphical Method
5.5 Using Excel’s Solver to Solve Linear Programming Problems
5.6 Analytic Solver
5.7 A Minimization Example—the Profit & Gambit co. Advertising-Mix Problem
Planning an Advertising Campaign
Formulating a Spreadsheet Model for This Problem
Applying Solver to This Model
The Mathematical Model in the Spreadsheet
5.8 Linear Programming from a Broader Perspective
5.9 Summary
Glossary
Learning Aids for This Chapter
Solved Problem
5.S1.Back Savers Production Problem
5.S2.Conducting a Marketing Survey
Problems
Case 5-1: Auto Assembly
Auto Assembly
Case 5-2: Cutting Cafeteria Costs
Cutting Cafeteria Costs
Case 5-3: Staffing a Call Center
Staffing a Call Center
Chapter 6: Linear Programming: Formulation and Applications
Introduction
6.1 A Case Study: The Super Grain Corp. Advertising-Mix Problem
The Problem
Analysis of the Problem
6.2 Resource-Allocation Problems
The Super Grain Corp. Advertising-Mix Problem
Characteristics of Resource-Allocation Problems
The Wyndor Glass Co. Product-Mix Problem
The TBA Airlines Problem
Capital Budgeting
Another Look at Resource Constraints
Summary of the Formulation Procedure for Resource-Allocation Problems
6.3 Cost–Benefit–Trade-Off Problems
The Profit & Gambit Co. Advertising-Mix Problem
Personnel Scheduling
Summary of the Formulation Procedure for Cost–Benefit–Trade-Off Problems
6.4 Mixed Problems
Super Grain Management Discusses Its Advertising-Mix Problem
Incorporating Additional Managerial Considerations into the Super Grain Model
Formulation of the Revised Spreadsheet Model
Other Applications of Linear Programming
Summary of the Formulation Procedure for Mixed Linear Programming Problems
6.5 Transportation Problems
The Big M Company Transportation Problem
6.6 Assignment Problems
An Example: The Sellmore Company Problem
Characteristics of Assignment Problems
6.7 Model Formulation from a Broader Perspective
6.8 Summary
Glossary
Learning Aids for This Chapter
Solved Problem
6.S1.Farm Management
6.S2.Diet Problem
6.S3.Cutting Stock Problem
6.S4.Producing and Distributing AEDs at Heart Start
6.S5.Bidding for Classes
Problems
Case 6-1: Shipping Wood to Market
Shipping Wood to Market
Case 6-2: Capacity Concerns
Capacity Concerns
Case 6-3: Fabrics and Fall Fashions
Fabrics and Fall Fashions
Case 6-4: New Frontiers
New Frontiers
Case 6-5: Assigning Students to Schools
Assigning Students to Schools
Case 6-6: Reclaiming Solid Wastes
Reclaiming Solid Wastes
Case 6-7: Project Pickings
Project Pickings
Chapter 7: The Art of Modeling with Spreadsheets
Introduction
7.1 A Case Study: The Everglade Golden Years Company Cash Flow Problem
7.2 Overview of The Process of Modeling with Spreadsheets
Plan: Visualize Where You Want to Finish
Plan: Do Some Calculations by Hand
Plan: Sketch Out a Spreadsheet
Build: Start with a Small Version of the Spreadsheet
Test: Test the Small Version of the Model
Build: Expand the Model to Full-Scale Size
Test: Test the Full-Scale Version of the Model
Analyze: Analyze the Model
Conclusion of the Case Study
7.3 Some Guidelines for Building “Good” Spreadsheet Models
Enter the Data First
Organize and Clearly Identify the Data
Enter Each Piece of Data into One Cell Only
Separate Data from Formulas
Keep It Simple
Use Range Names
Use Relative and Absolute References to Simplify Copying Formulas
Use Borders, Shading, and Colors to Distinguish between Cell Types
Show the Entire Model on the Spreadsheet
A Poor Spreadsheet Model
7.4 Debugging a Spreadsheet Model
7.5 Summary
Glossary
Learning Aids for This Chapter
Solved Problem
7.S1.Production and Inventory Planning Model
7.S2.Aggregate Planning: Manpower Hiring/Firing/Training
Problems
Case 7-1: Prudent Provisions for Pensions
Prudent Provisions for Pensions
Chapter 8: What-If Analysis for Linear Programming
Introduction
8.1 The Importance of What-If Analysis to Managers
8.2 Continuing the Wyndor Case Study
Management’s Discussion of the Recommended Product Mix
Summary of Management’s What-If Questions
8.3 The Effect of Changes in One Objective Function Coefficient
Using the Spreadsheet to Do Sensitivity Analysis
Using a Parameter Analysis Report Generated by Analytic Solver to Do Sensitivity Analysis Systematically
Using the Sensitivity Report to Find the Allowable Range
8.4 The Effect of Simultaneous Changes in Objective Function Coefficients
Using the Spreadsheet for This Analysis
Using a Two-Way Parameter Analysis Report Generated by Analytic Solver for This Analysis
Gleaning Additional Information from the Sensitivity Report
Comparisons
8.5 The Effect of Single Changes in a Constraint
Using the Spreadsheet for This Analysis
Using a Parameter Analysis Report Generated by Analytic Solver for This Analysis
Using the Sensitivity Report to Obtain the Key Information
Summary
8.6 The Effect of Simultaneous Changes in the Constraints
Using the Spreadsheet for This Analysis
Using a Parameter Analysis Report Generated by Analytic Solver for This Analysis
Gleaning Additional Information from the Sensitivity Report
8.7 Robust Optimization
Robust Optimization with Independent Parameters
Example
The General Procedure for Robust Optimization with Independent Parameters
8.8 Chance Constraints with Analytic Solver
Chance Constraints with the Uniform Distribution
Chance Constraints with the Normal Distribution
8.9 Summary
Glossary
Learning Aids for This Chapter
Solved Problem
8.S1.Sensitivity Analysis at Stickley Furniture
Problems
Case 8-1: Selling Soap
Selling Soap
Case 8-2: Controlling Air Pollution
Controlling Air Pollution
Case 8-3: Farm Management
Farm Management
Case 8-4: Assigning Students to Schools (Revisited)
Assigning Students to Schools (Revisited)
Chapter 9: Network Optimization Problems
Introduction
9.1 Minimum-Cost Flow Problems
An Example: The Distribution Unlimited Co. Problem
General Characteristics
Using Excel to Formulate and Solve Minimum-Cost Flow Problems
Solving Large Minimum-Cost Flow Problems More Efficiently
Some Applications
Special Types of Minimum-Cost Flow Problems
9.2 A Case Study: The Bmz Co. Maximum Flow Problem
Background
The Problem
Model Formulation
9.3 Maximum Flow Problems
General Characteristics
Continuing the Case Study with Multiple Supply Points and Multiple Demand Points
Some Applications
Solving Very Large Problems
9.4 Shortest Path Problems
An Example: The Littletown Fire Department Problem
General Characteristics
Some Applications
An Example of Minimizing Total Cost
An Example of Minimizing Total Time
9.5 Summary
Glossary
Learning Aids for This Chapter
Solved Problems
9.S1. Distribution at Heart Beats
9.S2.Assessing the Capacity of a Pipeline Network
9.S3.Driving to the Mile-High City
Problems
Case 9-1: Aiding Allies
Aiding Allies
Case 9-2: Money in Motion
Money in Motion
Case 9-3: Airline Scheduling
Airline Scheduling
Case 9-4: Broadcasting the Olympic Games
Broadcasting the Olympic Games
Chapter 10: Integer Programming
Introduction
10.1 The Nature of Integer Programming
10.2 A Case Study: The California Manufacturing Co. Problem
Background
Introducing Binary Decision Variables for the Yes-or-No Decisions
Dealing with Interrelationships between the Decisions
The BIP Model
Performing What-If Analysis
Management’s Conclusion
10.3 Using BIP for Project Selection: The Tazer Corp. Problem
The Tazer Corp. Problem
Formulation with Binary Variables
A BIP Spreadsheet Model for the Tazer Problem
10.4 Using BIP for The Selection of Sites For Emergency Services Facilities: The Caliente City Problem
The Caliente City Problem
Formulation with Binary Variables
A BIP Spreadsheet Model for the Caliente City Problem
10.5 Using BIP for Crew Scheduling: The Southwestern Airways Problem
The Southwestern Airways Problem
Formulation with Binary Variables
A BIP Spreadsheet Model for the Southwestern Airways Problem
10.6 Using Mixed BIP to Deal with Setup Costs for Initiating Production: The Revised Wyndor Problem
The Revised Wyndor Problem with Setup Costs
Formulation with Binary Variables
A Mixed BIP Spreadsheet Model for the Revised Wyndor Problem
10.7 Summary
Glossary
Learning Aids for This Chapter
Solved Problems
10.S1.Capital Budgeting with Contingency Constraints
10.S2.Locating Search-and-Rescue Teams
10.S3.Warehouse Site Selection
Problems
Case 10-1: Assigning Art
Assigning Art
Case 10-2: Stocking Sets
Stocking Sets
Case 10-3: Assigning Students to Schools (Revisited)
Assigning Students to Schools (Revisited)
Case 10-4: Broadcasting the Olympic Games (Revisited)
Broadcasting the Olympic Games (Revisited)
Chapter 11: Nonlinear Programming
Introduction
11.1 The Challenges of Nonlinear Programming
The Challenge of Nonproportional Relationships
The Challenge of Constructing Nonlinear Formulas
The Challenge of Solving Nonlinear Programming Models
11.2 Continuing the Wyndor Case Study To Deal with Decreasing Marginal Returns
Returning to the Wyndor Case Study
A Spreadsheet Formulation
11.3 Applying Nonlinear Programming to Portfolio Selection
11.4 Separable Programming
The Wyndor Glass Co. Problem When Overtime Is Needed
Applying Separable Programming to This Problem
Applying Separable Programming with Smooth Profit Graphs
The Wyndor Problem with Both Overtime Costs and Nonlinear Marketing Costs
11.5 Difficult Nonlinear Programming Problems
11.6 Evolutionary Solver and Genetic Algorithms
Selecting a Portfolio to Beat the Market
Applying Evolutionary Solver to Portfolio Selection to Beat the Market
Applying Evolutionary Solver to a Traveling Salesman Problem
Advantages and Disadvantages of Evolutionary Solver
11.7 Using Analytic Solver to Analyze a Model and Choose a Solving Method
Using Analytic Solver to Choose a Solving Method
Using Analytic Solver to Further Analyze the Model
11.8 Summary
Glossary
Learning Aids for This Chapter
Solved Problem
11.S1.Airline Ticket Pricing Model
Problems
Case 11-1: Continuation of the Super Grain Case Study
Continuation of the Super Grain Case Study
Case 11-2: Savvy Stock Selection
Savvy Stock Selection
Case 11-3: International Investments
International Investments
Chapter 12: Decision Analysis
Introduction
12.1 A Case Study: The Goferbroke Company Problem
Decision Analysis Terminology
The Payoff Table
12.2 Decision Criteria
Decision Making without Probabilities: The Maximax Criterion
Decision Making without Probabilities: The Maximin Criterion
Decision Making with Probabilities: The Maximum Likelihood Criterion
Decision Making with Probabilities: Bayes’ Decision Rule
12.3 Decision Trees
Spreadsheet Software for Decision Trees
12.4 Sensitivity Analysis with Decision Trees
Using a Data Table to Do Sensitivity Analysis Systematically
12.5 Checking Whether to Obtain More Information
12.6 Using New Information to Update the Probabilities
12.7 Using A Decision Tree to Analyze the Problem with a Sequence of Decisions
Constructing the Decision Tree
Performing the Analysis
Expected Value of Sample Information
Using Analytic Solver
12.8 Performing Sensitivity Analysis on the Problem with a Sequence of Decisions
Organizing the Spreadsheet
Using a Data Table to Do Sensitivity Analysis Systematically
12.9 Using Utilities to Better Reflect the Values of Payoffs
Utility Functions for Money
Dealing with the Goferbroke Co. Problem
Finding U(90)
The Equivalent Lottery Method for Determining Utilities
Constructing Max’s Utility Function for Money
Using a Decision Tree to Analyze the Problem with Utilities
Another Approach for Estimating U(M)
Using Analytic Solver with an Exponential Utility Function
12.10 The Practical Application of Decision Analysis
12.11 Summary
Glossary
Learning Aids for This Chapter
Solved Problems
12.S1.New Vehicle Introduction
12.S2.Settle or Go to Trial
Problems
Case 12-1: Who Wants to Be a Millionaire?
Who Wants to Be a Millionaire?
Case 12-2: University Toys and the Business Professor Action Figures
University Toys and the Business Professor Action Figures
Case 12-3: Brainy Business
Brainy Business
Case 12-4: Smart Steering Support
Smart Steering Support
Chapter 13: Queueing Models
Introduction
13.1 Elements of a Queueing Model
A Basic Queueing System
An Example
Arrivals
The Exponential Distribution for Interarrival Times
The Queue
Service
Some Service-Time Distributions
Labels for Queueing Models
Summary of Model Assumptions
13.2 Some Examples of Queueing Systems
13.3 Measures of Performance for Queueing Systems
Choosing a Measure of Performance
Defining the Measures of Performance
Relationships between L, W, Lq, and Wq
Using Probabilities as Measures of Performance
13.4 A Case Study: the Dupit Corp. Problem
Some Background
The Issue Facing Top Management
Alternative Approaches to the Problem
The Analytics Team’s View of the Problem
13.5 Some Single-Server Queueing Models
The M/M/ 1 Model
Applying the M/M/ 1 Model to the Case Study under the Current Policy
Applying the M/M/1 Model to John Phixitt’s Suggested Approach
The M/G/1 Model
Applying the M/G/1 Model to the Approach Suggested by the Vice President for Engineering
13.6 Some Multiple-Server Queueing Models
The M/M/s Model
Applying These Models to the Approach Suggested by the Chief Financial Officer
The M/D/s Model
13.7 Priority Queueing Models
A Preemptive Priorities Queueing Model
A Nonpreemptive Priorities Queueing Model
Applying the Nonpreemptive Priorities Queueing Model to the Approach Suggested by the Vice President for Marketing
Management’s Conclusions
13.8 Some Insights About Designing Queueing Systems
13.9 Economic Analysis of The Number of Servers to Provide
An Example
13.10 Behavioral Queueing Theory
13.11 Summary
Glossary
Key Symbols
Learning Aids for This Chapter
Solved Problem
Problems
Case 13-1: Queueing Quandary
Queueing Quandary
Case 13-2: Reducing In-Process Inventory
Reducing In-Process Inventory
Chapter 14: Computer Simulation: Basic Concepts
Introduction
14.1 The Essence of Computer Simulation
The Role of Computer Simulation
Example 1: A Coin-Flipping Game
Example 2: Corrective Maintenance versus Preventive Maintenance
Generating Random Observations from a Probability Distribution
14.2 A Case Study: Herr Cutter’s Barber Shop (Revisited)
The Decision Facing Herr Cutter
Gathering Data
Generating Random Observations from These Probability Distributions
The Building Blocks of a Simulation Model for a Stochastic System
Illustrating the Computer Simulation Process
Estimating Measures of Performance
Simulating the Barber Shop with an Associate
14.3 Analysis of the Case Study
The Financial Factors
Analysis of Continuing without an Associate
Testing the Validity of the Simulation Model
Analysis of the Option of Adding an Associate
14.4 Outline of a Major Computer Simulation Study
Step 1: Formulate the Problem and Plan the Study
Step 2: Collect the Data and Formulate the Simulation Model
Step 3: Check the Accuracy of the Simulation Model
Step 4: Select the Software and Construct the Model
Step 5: Test the Validity of the Simulation Model
Step 6: Plan the Simulations to Be Performed
Step 7: Conduct the Simulation Runs and Analyze the Results
Step 8: Present Recommendations to Management
14.5 Summary
Glossary
Learning Aids for This Chapter
Solved Problem
14.S1.Estimating the Cost of Insurance Claims
Problems
Case 14-1: Planning Planers
Planning Planers
Case 14-2: Reducing In-Process Inventory (Revisited)
Reducing In-Process Inventory (Revisited)
Chapter 15: Computer Simulation with Analytic Solver
Introduction
15.1 A Case Study: Freddie the Newsboy’s problem
Freddie’s Problem
A Spreadsheet Model for This Problem
The Application of Analytic Solver
How Accurate Are the Simulation Results?
Freddie’s Conclusions
15.2 Bidding for a Construction Project: A Prelude to the Reliable Construction Co. Case Study
The Reliable Construction Co. Bidding Problem
A Spreadsheet Model for Applying Computer Simulation
The Simulation Results
15.3 Project Management: Revisiting the Reliable Construction Co. Case Study
The Problem Being Addressed
A Spreadsheet Model for Applying Computer Simulation
The Simulation Results
A Key Insight Provided by the Sensitivity Chart
15.4 Financial risk analysis: Revisiting the think-big development co. problem
The Think-Big Financial Risk Analysis Problem
A Spreadsheet Model for Applying Computer Simulation
The Simulation Results
15.5 Revenue Management in the Travel Industry
The Transcontinental Airlines Overbooking Problem
A Spreadsheet Model for Applying Computer Simulation
The Simulation Results
15.6 Choosing the Right Distribution
Characteristics of the Available Distributions
Identifying the Continuous Distribution That Best Fits Historical Data
15.7 Decision Making with Parameter Analysis Reports and Trend Charts
A Parameter Analysis Report and Trend Chart for the Case Study
A Parameter Analysis Report for the Reliable Construction Co. Bidding Problem
A Parameter Analysis Report and Trend Chart for the Transcontinental Airlines Overbooking Problem
15.8 Optimizing with Computer Simulation Using the Solver in Analytic Solver
Application of Computer Simulation and Solver to the Case Study
Application of Computer Simulation and Solver to a Project Selection Example
15.9 Summary
Glossary
Learning Aids for This Chapter
Solved Problem
15.S1.Saving for Retirement
Problems
Case 15-1: Action Adventures
Action Adventures
Case 15-2: Pricing under Pressure
Pricing under Pressure
Case 15-3: Financial Planning for Retirement
Financial Planning for Retirement
Appendix A: Tips for Using Microsoft Excel for Modeling
Introduction
Anatomy of the Microsoft Excel Window
Working with Workbooks
Working with Worksheets
Using Worksheets with Solver
Using Worksheets with Analytic Solver Decision Trees
Using Worksheets with Analytic Solver Simulation Models
Working with Cells
Selecting Cells
Entering or Editing Data, Text, and Formulas into Cells
Moving or Copying Cells
Filling Cells
Relative and Absolute References
Using Range Names
Formatting Cells
Appendix B: Partial Answers to Selected Problems
Introduction
CHAPTER 2
CHAPTER 3
CHAPTER 4
CHAPTER 5
CHAPTER 6
CHAPTER 7
CHAPTER 8
CHAPTER 9
CHAPTER 10
CHAPTER 11
CHAPTER 12
CHAPTER 13
CHAPTER 14
CHAPTER 15
Index
Frederick Hillier – Professor emeritus of operations research at Stanford University. Dr. 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.
Mark Hillier – Associate professor of quantitative methods at the School of Business at the University of Washington. Dr. Hillier received his BS in engineering (plus a concentration in computer science) from Swarthmore College, and he received his MS with distinction in operations research and PhD in industrial engineering and engineering management from Stanford University. As an undergraduate, he won the McCabe Award for ranking first in his engineering class, won election to Phi Beta Kappa based on his work in mathematics, set school records on the men’s swim team, and was awarded two national fellowships (National Science Foundation and Tau Beta Pi) for graduate study. During that time, he also developed a comprehensive software tutorial package, OR Courseware, for the Hillier-Lieberman textbook, Introduction to Operations Research. As a graduate student, he taught a PhD-level seminar in operations management at Stanford and won a national prize for work based on his PhD dissertation. At the University of Washington, he currently teaches courses in management science and spreadsheet modeling.
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