**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

READANYWHERE

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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