Case 4.1 Blending Aviation Gasoline at Jansen Gas, page 214 For the project, you will write a proposal for the case study and submit the

07 Apr Case 4.1 Blending Aviation Gasoline at Jansen Gas, page 214 For the project, you will write a proposal for the case study and submit the

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Case 4.1 Blending Aviation Gasoline at Jansen Gas, page 214

For the project, you will write a proposal for the case study and submit the final product. The final product will be an Excel spreadsheet used to create the model and either a Word document or a Power Point presentation. The final project will be graded not only on the accuracy of the quantitative solutions, but also the analytical approach used and the presentation of the results. Keep in mind that this course is designed for individuals interested in Business Management. As such, the final presentation should be appropriate for a presentation in a professional setting. It will be necessary to clearly explain the case study and present the results in a professional, yet easily understood manner.

To guide you with the preparation of the your final project (case study) presentation there is a guideline document that will provide further detail relative organization, content, and information flow for your presentation. Please select the following link (case study) to access this important information. Be sure to use this information as your guide when writing your presentation.

The Case Study Presentation

The objective of the case study presentation is for you to: Reflect on your problem solving process, define and discuss your analysis methodology, define and discuss your results, and provide meaningful conclusions and/or recommendations. Your interpretation of the quantitative analysis process and interpretation of results is a critical measure of your comprehension of the course content and ability to apply the knowledge and analytical tools that you have been studying.

To assist you with the development of your presentation organization, the following content outline is being provided. Your presentation should follow the outline guidelines to ensure that you focus on all the important content elements in a logic manner and your submission is appropriately evaluated.

Your presentation can be either an MS Power Point document or an MS Word document. The organization and flow of content would be the same for either document. The MS Power Point document would be more aligned with a real presentation model (no essay type content) versus the MS Document model (essay type content).

Presentation: Content Outline Guidelines

Presentation: Slides (Power Point) Pages (MS Word Doc)	Description of Content
Title Page	· Case Study Title · Student Name · Course Name · Instructor: Name · Date
Table of Contents	Power Point: Provide a list of primary topics to be presented. Word Document: Provide a list of sub-topics (and starting page number) within the presentation
Executive Summary (aka Abstract)	A one- or two-paragraph statement summing up the Case Study (what, why, when, where, how, and who). Introduces the project, critical questions, problem statement, and provides highlights of the important findings.
Introduction (aka Background)	Provides information leading up to the Case Study that will help to position the importance of the study. Describes what you did, and why it was of interest—and tries to influence the reader’s interest. Typically several paragraphs. However, the Introduction can be limited by the extent of background information that you have.
Problem Statement	Presents the problem statement that the case study is focusing on and that is the basis of your analysis. May include question(s) that led to development of the problem statement. Substantiate the problem statement by provide inquiry questions that support the need for further analysis. Questions will logically serve as stepping-stones for the analysis methodology.
Methodology	Describes the step-by-step procedure employed and explains why it is appropriate to this case study, Provide details: what quantitative model and analytical tools will be used, what data is required (inputs, constraints, assumptions, etc.). How will data be processed / used within the context of the selected model. What analysis techniques are required to enable effective analytical processes for useable, quantifiable results?
Analysis	An explanation of what the information you collected by means of using your model and effectively using Excel analytical tools. Discuss analytical results of Excel add-ins, identify trends, data constraints and any additional data analysis necessary to obtain results for meaningful assessment / evaluation leading to valid conclusions and recommendations. Note: The results (above) are just “facts;” the analysis is your interpretation (or opinion) of what the facts mean.
Results	Presents the data that were collected (so the reader can make a judgment about your work based on same information you used); the data should be organized and processed to some extent for clarity but should not be just a summary. Be sure you are addressing each question that the case studies is requiring you to respond to.
Conclusions and Recommendations	A fairly concise statement of what the case study analysis found and what you have learned. Provide any recommendation or conclusions consistent with the cases study problem requirements. What are your “lessons learned” from this case study?
Appendix	To include extra graphs, charts, detailed analysis data, spreadsheets. Be sure to reference these documents throughout your presentation.
References	Provide a list of applicable references. Include only citations to materials that were critical to your case study.

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Copyright 2019 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.

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Practical Management Science

Wayne L. Winston Kelley School of Business, Indiana University

S. Christian Albright Kelley School of Business, Indiana University

6th Edition

Australia ● Brazil ● Mexico ● Singapore ● United Kingdom ● United States

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This is an electronic version of the print textbook. Due to electronic rights restrictions, some third party content may be suppressed. Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. The publisher reserves the right to remove content from this title at any time if subsequent rights restrictions require it. For valuable information on pricing, previous editions, changes to current editions, and alternate formats, please visit www.cengage.com/highered to search by ISBN#, author, title, or keyword for materials in your areas of interest.

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Practical Management Science, Sixth Edition

Wayne L. Winston, S. Christian Albright

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To Mary, my wonderful wife, best friend, and constant companion And to our Welsh Corgi, Bryn, who still just wants to play ball S.C.A.

To my wonderful family Vivian, Jennifer, and Gregory W.L.W.

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S. Christian Albright got his B.S. degree in Mathematics from Stanford in 1968 and his Ph.D. degree in Operations Research from Stanford in 1972. Until his retirement in 2011, he taught in the Operations & Decision Technologies Department in the Kelley School of Business at Indiana University. His teaching included courses in management science, computer simulation, and statis- tics to all levels of business students: undergraduates, MBAs, and doctoral students. He has published over 20 articles in leading operations research journals in the area of applied probability, and he has authored several books, including Practical Manage-

ment Science, Data Analysis and Decision Making, Data Analysis for Managers, Spread- sheet Modeling and Applications, and VBA for Modelers. He jointly developed StatTools, a statistical add-in for Excel, with the Palisade Corporation. In “retirement,” he continues to revise his books, and he has developed a commercial product, ExcelNow!, an extension of the Excel tutorial that accompanies this book. On the personal side, Chris has been married to his wonderful wife Mary for 46 years. They have a special family in Philadelphia: their son Sam, his wife Lindsay, and their two sons, Teddy and Archer. Chris has many interests outside the academic area. They include activities with his family (especially traveling with Mary), going to cultural events, power walking, and reading. And although he earns his livelihood from statistics and management science, his real passion is for playing classical music on the piano.

Wayne L. Winston is Professor Emeritus of Decision Sciences at the Kelley School of Business at Indiana University and is now a Professor of Decision and Information Sciences at the Bauer College at the University of Houston. Winston received his B.S. degree in Mathematics from MIT and his Ph.D. degree in Operations Research from Yale. He has written the successful textbooks Operations Research: Applications and Algorithms, Mathematical Programming: Applications and Algorithms, Simulation Modeling with @RiSk, Practical Management Science, Data Analysis for Managers, Spreadsheet

Modeling and Applications, Mathletics, Data Analysis and Business Modeling with Excel 2013, Marketing Analytics, and Financial Models Using Simulation and Optimization. Winston has published over 20 articles in leading journals and has won more than 45 teaching awards, including the school-wide MBA award six times. His current interest is in showing how spreadsheet models can be used to solve business problems in all disciplines, particularly in finance, sports, and marketing. Wayne enjoys swimming and basketball, and his passion for trivia won him an appearance several years ago on the television game show Jeopardy, where he won two games. He is married to the lovely and talented Vivian. They have two children, Gregory and Jennifer.

About the Authors

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vii

Preface xiii

1 Introduction to Modeling 1

2 Introduction to Spreadsheet Modeling 19

3 Introduction to Optimization Modeling 71

4 Linear Programming Models 135

5 Network Models 219

6 Optimization Models with Integer Variables 277

7 Nonlinear Optimization Models 339

8 Evolutionary Solver: An Alternative Optimization Procedure 407

9 Decision Making under Uncertainty 457

10 Introduction to Simulation Modeling 515

11 Simulation Models 589

12 Queueing Models 667

13 Regression and Forecasting Models 715

14 Data Mining 771

References 809

Index 815

MindTap Chapters 15 Project Management 15-1

16 Multiobjective Decision Making 16-1

17 Inventory and Supply Chain Models 17-1

Brief Contents

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Preface xiii

CHAPTER 1 Introduction to Modeling 1 1.1 Introduction 3 1.2 A Capital Budgeting Example 3 1.3 Modeling versus Models 6 1.4 A Seven-Step Modeling Process 7 1.5 A Great Source for Management Science

Applications: Interfaces 13 1.6 Why Study Management Science? 13 1.7 Software Included with This Book 15 1.8 Conclusion 17

CHAPTER 2 Introduction to Spreadsheet Modeling 19

2.1 Introduction 20 2.2 Basic Spreadsheet Modeling:

Concepts and Best Practices 21 2.3 Cost Projections 25 2.4 Breakeven Analysis 31 2.5 Ordering with Quantity Discounts

and Demand Uncertainty 39 2.6 Estimating the Relationship between

Price and Demand 44 2.7 Decisions Involving the Time Value of

Money 54 2.8 Conclusion 59 Appendix Tips for Editing and

Documenting Spreadsheets 64 Case 2.1 Project Selection at Ewing Natural

Gas 66 Case 2.2 New Product Introduction at eTech 68

CHAPTER 3 Introduction to Optimization Modeling 71

3.1 Introduction 72 3.2 Introduction to Optimization 73 3.3 A Two-Variable Product Mix Model 75

Contents

3.4 Sensitivity Analysis 87 3.5 Properties of Linear Models 97 3.6 Infeasibility and Unboundedness 100 3.7 A Larger Product Mix Model 103 3.8 A Multiperiod Production Model 111 3.9 A Comparison of Algebraic

and Spreadsheet Models 120 3.10 A Decision Support System 121 3.11 Conclusion 123 Appendix Information on Optimization Software 130 Case 3.1 Shelby Shelving 132

CHAPTER 4 Linear Programming Models 135 4.1 Introduction 136 4.2 Advertising Models 137 4.3 Employee Scheduling Models 147 4.4 Aggregate Planning Models 155 4.5 Blending Models 166 4.6 Production Process Models 174 4.7 Financial Models 179 4.8 Data Envelopment Analysis (DEA) 191 4.9 Conclusion 198 Case 4.1 Blending Aviation Gasoline at Jansen

Gas 214 Case 4.2 Delinquent Accounts at GE Capital 216 Case 4.3 Foreign Currency Trading 217

CHAPTER 5 Network Models 219 5.1 Introduction 220 5.2 Transportation Models 221 5.3 Assignment Models 233 5.4 Other Logistics Models 240 5.5 Shortest Path Models 249 5.6 Network Models in the Airline Industry 258 5.7 Conclusion 267 Case 5.1 Optimized Motor Carrier Selection at

Westvaco 274

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CHAPTER 9 Decision Making under Uncertainty 457

9.1 Introduction 458 9.2 Elements of Decision Analysis 460 9.3 Single-Stage Decision Problems 467 9.4 The PrecisionTree Add-In 471 9.5 Multistage Decision Problems 474 9.6 The Role of Risk Aversion 492 9.7 Conclusion 499 Case 9.1 Jogger Shoe Company 510 Case 9.2 Westhouser Paper Company 511 Case 9.3 Electronic Timing System for

Olympics 512 Case 9.4 Developing a Helicopter Component

for the Army 513

CHAPTER 10 Introduction to Simulation Modeling 515

10.1 Introduction 516 10.2 Probability Distributions for Input

Variables 518 10.3 Simulation and the Flaw of Averages 537 10.4 Simulation with Built-in Excel Tools 540 10.5 Introduction to @RISK 551 10.6 The Effects of Input Distributions on

Results 568 10.7 Conclusion 577 Appendix Learning More About @RISK 583 Case 10.1 Ski Iacket Production 584 Case 10.2 Ebony Bath Soap 585 Case 10.3 Advertising Effectiveness 586 Case 10.4 New Project Introduction at eTech 588

CHAPTER 11 Simulation Models 589 11.1 Introduction 591 11.2 Operations Models 591 11.3 Financial Models 607 11.4 Marketing Models 631 11.5 Simulating Games of Chance 646 11.6 Conclusion 652 Appendix Other Palisade Tools for Simulation 662

x Contents

CHAPTER 6 Optimization Models with Integer Variables 277

6.1 Introduction 278 6.2 Overview of Optimization with Integer

Variables 279 6.3 Capital Budgeting Models 283 6.4 Fixed-Cost Models 290 6.5 Set-Covering and Location-Assignment

Models 303 6.6 Cutting Stock Models 320 6.7 Conclusion 324 Case 6.1 Giant Motor Company 334 Case 6.2 Selecting Telecommunication Carriers to

Obtain Volume Discounts 336 Case 6.3 Project Selection at Ewing Natural Gas 337

CHAPTER 7 Nonlinear Optimization Models 339 7.1 Introduction 340 7.2 Basic Ideas of Nonlinear Optimization 341 7.3 Pricing Models 347 7.4 Advertising Response and Selection Models 365 7.5 Facility Location Models 374 7.6 Models for Rating Sports Teams 378 7.7 Portfolio Optimization Models 384 7.8 Estimating the Beta of a Stock 394 7.9 Conclusion 398 Case 7.1 GMS Stock Hedging 405

CHAPTER 8 Evolutionary Solver: An Alternative Optimization Procedure 407

8.1 Introduction 408 8.2 Introduction to Genetic Algorithms 411 8.3 Introduction to Evolutionary Solver 412 8.4 Nonlinear Pricing Models 417 8.5 Combinatorial Models 424 8.6 Fitting an S-Shaped Curve 435 8.7 Portfolio Optimization 439 8.8 Optimal Permutation Models 442 8.9 Conclusion 449 Case 8.1 Assigning MBA Students to Teams 454 Case 8.2 Project Selection at Ewing Natural Gas 455

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Contents xi

Case 11.1 College Fund Investment 664 Case 11.2 Bond Investment Strategy 665 Case 11.3 Project Selection Ewing Natural Gas 666

CHAPTER 12 Queueing Models 667 12.1 Introduction 668 12.2 Elements of Queueing Models 670 12.3 The Exponential Distribution 673 12.4 Important Queueing Relationships 678 12.5 Analytic Steady-State Queueing Models 680 12.6 Queueing Simulation Models 699 12.7 Conclusion 709 Case 12.1 Catalog Company Phone Orders 713

CHAPTER 13 Regression and Forecasting Models 715 13.1 Introduction 716 13.2 Overview of Regression Models 717 13.3 Simple Regression Models 721 13.4 Multiple Regression Models 734 13.5 Overview of Time Series Models 745 13.6 Moving Averages Models 746 13.7 Exponential Smoothing Models 751 13.8 Conclusion 762 Case 13.1 Demand for French Bread at Howie’s

Bakery 768 Case 13.2 Forecasting Overhead at Wagner

Printers 769 Case 13.3 Arrivals at the Credit Union 770

CHAPTER 14 Data Mining 771 14.1 Introduction 772 14.2 Classification Methods 774 14.3 Clustering Methods 795 14.4 Conclusion 806 Case 14.1 Houston Area Survey 808

References 809

Index 815

MindTap Chapters

CHAPTER 15 Project Management 15-1 15.1 Introduction 15-2 15.2 The Basic CPM Model 15-4 15.3 Modeling Allocation of Resources 15-14 15.4 Models with Uncertain Activity Times 15-30 15.5 A Brief Look at Microsoft Project 15-35 15.6 Conclusion 15-39

CHAPTER 16 Multiobjective Decision Making 16-1 16.1 Introduction 16-2 16.2 Goal Programming 16-3 16.3 Pareto Optimality and Trade-Off Curves 16-12 16.4 The Analytic Hierarchy Process (AHP) 16-20 16.5 Conclusion 16-25

CHAPTER 17 Inventory and Supply Chain Models 17-1 17.1 Introduction 17-2 17.2 Categories of Inventory and Supply Chain

Models 17-3 17.3 Types of Costs in Inventory and Supply Chain

Models 17-5 17.4 Economic Order Quantity (EOQ) Models 17-6 17.5 Probabilistic Inventory Models 17-21 17.6 Ordering Simulation Models 17-34 17.7 Supply Chain Models 17-40 17.8 Conclusion 17-50 Case 17.1 Subway Token Hoarding 17-57

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xiii

Practical Management Science provides a spreadsheet- based, example-driven approach to management science. Our initial objective in writing the book was to reverse negative attitudes about the course by making the subject relevant to students. We intended to do this by imparting valuable modeling skills that students can appreciate and take with them into their careers. We are very gratified by the success of previous editions. The book has exceeded our initial objectives. We are especially pleased to hear about the success of the book at many other colleges and universities around the world. The acceptance and excitement that has been generated has motivated us to revise the book and make the current edition even better. When we wrote the first edition, management science courses were regarded as irrelevant or uninteresting to many business students, and the use of spreadsheets in management science was in its early stages of development. Much has changed since the first edition was published in 1996, and we believe that these changes are for the better. We have learned a lot ab

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