DE001D461CD159BE1375DC125727A00529614

MANAGERIAL DECISIONMODELING WITHSPREADSHEETSSECOND EDITION

Nagraj (Raju) BalakrishnanProfessor of ManagementClemson University

Barry RenderCharles Harwood Professor of Management ScienceGraduate School of Business, Rollins College

Ralph M. Stair, Jr.Professor Emeritus of Management Information SystemsFlorida State University

PEARSON

PrenticeHall

PRENTICE HALLUpper Saddle River, New Jersey 07458

© 2008 AGI-Information Management Consultants

May be used for personal purporses only or by libraries associated to dandelon.com network.

CHAPTER 1

1.1

1.2

1.3

1.4

1.5

1.6

1.7

CHAPTER 2

2.12.2

Introduction to ManagerialDecision Making 1What is Decision Modeling? 2Types of Decision Models 2Deterministic Models 2

Probabilistic Models 3

Quantitative versus Qualitative Data 4

Using Spreadsheets in DecisionModeling 4

Steps Involved in Decision Modeling 6

Step 1: Formulation 6

Step 2: Solution 8

Step 3: Interpretation and SensitivityAnalysis 8

Decision Modeling in the Real World 9

Spreadsheet Example of a DecisionModel: Tax Computation 9

Spreadsheet Example of AnotherDecision Model: Break-Even Analysis 13

Using Goal Seek to Find the Break-EvenPoint 15

Possible Problems in Developing DecisionModels 17

Defining the Problem 17

Developing a Model 17

Acquiring Input Data 18

Developing a Solution 18

Testing the Solution 19

Analyzing the Results 19

Implementation—Not Just the FinalStep 19Summary 20 Glossary 20 DiscussionQuestions and Problems 20 Bibliography 22

Linear Programming Models:Graphical and ComputerMethods 23Introduction 24Development of a Linear ProgrammingModel 25Formulation 25

Solution 25

Interpretation and Sensitivity Analysis 25

Properties of a Linear ProgrammingModel 25

Basic Assumptions of a Linear ProgrammingModel 26

2.3 Formulating a Lhiear ProgrammingProblem 27

Linear Programming Example:Flair Furniture Company 28

Decision Variables 28

The Objective Function 29

Constraints 29

Nonnegativity Constraints and IntegerValues 30

Guidelines to Developing a Correct LPModel 30

2.4 Graphical Solution of a LinearProgramming Problem with TwoVariables 32

Graphical Representationof Constraints 32

Feasible Region 35

Identifying an Optimal Solution by UsingLevel Lines 36

Identifying an Optimal Solution by UsingAll Corner Points 39

Comments on Flair Furniture's OptimalSolution 39

Extension to Flair Furniture'sLP Model 40

2.5 A Minimization Linear ProgrammingProblem 41

Holiday Meal Turkey Ranch 42

Graphical Solution of the Holiday MealTurkey Ranch Problem 43

2.6 Special Situations in Solving LinearProgramming Problems 44

Redundant Constraints 45

Infeasibility 46

Alternate Optimal Solutions 46

Unbounded Solution 47

2.7 Setting Up and Solving LinearProgramming Problems UsingExcel's Solver 49

Using Solver to Solve the Flair FurnitureProblem 49

Vlll Contents

Changing Cells 50

Target Cell 51

Constraints 52

Entering Information in Solver 53Using Solver to Solve Flair Furniture

Company's Modified Problem 58

Using Solver to Solve the Holiday MealTurkey Ranch Problem 59

2.8 Premium Solver for Education 61

2.9 Algorithmic Solution Procedures forLinear Programming Problems 63

Simplex Method 63Karmarkar's Algorithm 63Summary 64 Glossary 64 SolvedProblems 65 Discussion Questions andProblems 68 Case Study: Mexicana WireWinding, Inc. 74 Case Study: GoldingLandscaping and Plants, Inc. 75Bibliography 76

CHAPTER 4

4.1

4.2

4.3

CHAPTER 3

3.1

3.2

3.3

3.4

3.5

3.6

3.7

3.8

Linear Programming ModelingApplications with ComputerAnalyses in Excel 79Introduction 80

Manufacturing Applications 81

Product Mix Problem 81

Make-Buy Decision Problem 84

Marketing Applications 88

Media Selection Problem 88

Marketing Research Problem 90

Finance Applications 93

Portfolio Selection Problem 93

Alternate Formulations of the PortfolioSelection Problem 96

Employee Staffing Applications 98

Labor Planning Problem 98

Extensions to the Labor Planning ,Problem 100

Assignment Problem 101

Transportation Applications 101

Vehicle Loading Problem 101

Expanded Vehicle LoadingProblem—Allocation Problem 103

Transportation Problem 105

Blending Applications 106

Diet Problem 106

Blending Problem 108

Multiperiod Applications 112

Production Scheduling Problem 112

Sinking Fund Problem 118

4.4

A Z

4.6

4.7

CHAPTER 5

5.1

Summary 121 Solved Problem 122Problems 124 Case Study: ChaseManhattan Bank 137 Bibliography 137

Linear Programming SensitivityAnalysis 139Introduction 140

Why Do We Need to Study SensitivityAnalysis? 140

Sensitivity Analysis Using Graphs 141

Types of Sensitivity Analysis 142

Impact of Changes in an Objective FunctionCoefficient 143

Impact of Changes in a Constraint'sRight-Hand-Side Value 145

Sensitivity Analysis Using SolverReports 149

Solver Reports 150

Sensitivity Report 151

Impact of Changes in a Constraint'sRight-Hand-Side Value 152

Impact of Changes in an ObjectiveFunction Coefficient 153

Sensitivity Analysis for a LargerMaximization Example 156

Anderson Electronics Example 156

Some Questions We Want Answered 156


Analyzing Simultaneous Changesby Using the 100% Rule 161

Simultaneous Changes in ConstraintRHS Values 161

Simultaneous Changes in OFC Values 162

Pricing Out New Variables 162

Anderson's Proposed New Product 162

Sensitivity Analysis for a MinimizationExample 165

Burn-Off Diet Drink Example 165

Burn-Off's Excel Solution 165

Answering Sensitivity Analysis Questionsfor Burn-Off 167

Summary 168 Glossary 168 SolvedProblem 169 Discussion Questionsand Problems 172 Case Study: CoastalStates Chemicals and Fertilizers 182Bibliography 183

Transportation, Assignment,and Network Models 185Introduction 186

Transportation Model 186

Transshipment Model 186

Contents IX

Assignment Model 186

Maximal-Flow Model 186

Shortest-Path Model 187

Minimal-Spanning Tree Model 187

5.2 Characteristics of Network Models 187

Types of Arcs 187

Types of Nodes 188

5.3 Transportation Model 190

LP Formulation for Executive Furniture'sTransportation Model 191

Solving the Transportation Model UsingExcel 193

Alternate Excel Layout for theTransportation Model 193

Unbalanced Transportation Models 194


An Application of the TransportationModel: Facility Location 197

5.4 Transportation Models with Max~MinandMin -Max Objectives 197

5.5 Transshipment Model 200

Executive Furniture CompanyExample—Revisited 200

LP Formulation for Executive Furniture'sTransshipment Model 201

5.6 Assignment Model 203

Fix-It Shop Example 203

Solving Assignment Models 204

LP Formulation for Fix-It Shop's AssignmentModel 205

5.7 Maximal-Flow Model 207

Road System in Waukesha, Wisconsin 207

LP Formulation for Waukesha Road System'sMaximal-Flow Model 208

5.8 Shortest-Path Model 211Ray Design Inc. Example 212

LP Formulation for Ray Design Inc.'s 'Shortest-Path Model 212

5.9 Minimal-Spanning Tree Model 214

Lauderdale Construction CompanyExample 214

Summary 217 Glossary 217 SolvedProblems 218 Discussion Questions andProblems 224 Case Study: Old OregonWood Store 232 Case Study: Custom VansInc. 233 Case Study: Binder's Beverage 235Bibliography 236

CHAPTER 6 Integer, Goal, and NonlinearProgramming Models 237

6.1 Introduction 238

Integer Programming Models 238

Goal Programming Models 238

Nonlinear Programming Models 238

6.2 Models with General IntegerVariables 239

Harrison Electric Company 239

Using Solver to Solve Models with GeneralInteger Variables 242

How Are IP Models Solved? 244

Solver Options 245

Should We Include Integer Requirements ina Model? 247

6.3 Models with Binary Variables 248

Portfolio Selection at Simkin andSteinberg 248

Set Covering Problem at SussexCounty 252

6.4 Mixed Integer Models: Fixed-ChargeProblems 254

Locating a New Factory for HardgraveMachine Company 255

6.5 Goal Programming Models 259

Goal Programming Example: Wilson DoorsCompany 259

Solving Goal Programming Modelswith Weighted Goals 262

Solving Goal Programming Modelswith Ranked Goals 265

Comparing the Two Approaches for SolvingGP Models 271

6.6 Nonlinear Programming Models 271

Why Are NLP Models Difficult to Solve? 271

Solving Nonlinear Programming ModelsUsing Solver 273

Computational Procedures for NonlinearProgramming Problems 278

Summary 278 Glossary 279 SolvedProblems 279 Discussion Questions andProblems 285 Case Study: SchankMarketing Research 292 Case Study:Oakton River Bridge 292 Case Study:PuyallupMall 293 Bibliography 294

CHAPTER 7 Project Management 2957.1 Introduction 296

Phases in Project Management 296

Use of Software Packages in ProjectManagement 299

7.2 Project Networks 300

Identifying Activities 300

Identifying Activity Times and OtherResources 301

L

Contents

7.3

7.4

7.5

7.6

7.7

CHAPTER 88.1

8.2

8.3

8.4

Project Management Techniques:PERT and CPM 301

Project Management Example:General Foundry, Inc. 302

Drawing the Project Network 303

Determining the Project Schedule 305

Forward Pass 306

Backward Pass 308

Calculating Slack Time and Identifyingthe Critical Path(s) 310

Total Slack Time versus Free Slack Time 311

Variability in Activity Times 313

PERT Analysis 313

Probability of Project Completion 316

Determining Project Completion Timefor a Given Probability 317

Variability in Completion Timeof Noncritical Paths 318

Managing Project Costs and OtherResources 319

Planning and Scheduling Project Costs:Budgeting Process 319

Monitoring and Controlling ProjectCosts 321

Managing Other Resources 323

Project Crashing 324Crashing General Foundry's Project

(Hand Calculations) 326

Crashing General Foundry's Project UsingLinear Programming 329

Using Linear Programming to DetermineEarliest and Latest Starting Times 331

Using Microsoft Project to ManageProjects 332

Creating a Project Schedule Using MicrosoftProject 333

Tracking Progress and Managing CostsUsing Microsoft Project 336 <

Summary 340 Glossary 340 SolvedProblems 341 Discussion Questions andProblems 342 Case Study: HaygoodBrothers Construction Company 350Case Study: Family Planning ResearchCenter of Nigeria 351 Bibliography 352

8.5

8.7

8.8

8.9

8.10

Decision Analysis 355Introduction 356The Five Steps in Decision Analysis 356Thompson Lumber Company Example 356

Types of Decision-MakingEnvironments 358Decision Making Under Uncertainty 360

CHAPTER 99.1

9.2

9.3

Maximax Criterion 361

Maximin Criterion 361

Criterion of Realism (Hurwicz) 361

Equally Likely (Laplace) Criterion 362

Minimax Regret Criterion 362

Using Excel to Solve Decision-MakingProblems under Uncertainty 363

Decision Making Under Risk 365

Expected Monetary Value 365

Expected Opportunity Loss 366

Expected Value of Perfect Information 367

Using Excel to Solve Decision-MakingProblems under Risk 368

Decision Trees 368

Folding Back a Decision Tree 370

Using TreePlan to Solve Decision TreeProblems with Excel 372

Loading TreePlan 372

Creating a Decision Tree Using TreePlan 372

Decision Trees for MultistageDecision-Making Problems 377

A Multistage Decision-Making Problemfor Thompson Lumber 378

Expanded Decision Tree for ThompsonLumber 378

Folding Back the Expanded Decision Treefor Thompson Lumber 380

Expected Value of Sample Information 382

Estimating Probability Values UsingBayesian Analysis 383

Calculating Revised Probabilities 384

Potential Problems in Using SurveyResults 386

Utility Theory 386

Measuring Utility and Constructinga Utility Curve 387

Utility as a Decision-Making Criterion 390

Summary 392 Glossary 392 SolvedProblems 393 Discussion Questions andProblems 397 Case Study: Ski Right 404Case Study: Blake Electronics 405Bibliography 407

Queuing Models 409Introduction 410

Approaches for Analyzing Queues 410

Queuing System Costs 411Characteristics of a Queuing System 412Arrival Characteristics 412

Queue Characteristics 415

Contents XI

Service Facility Characteristics 416Measuring the Queue's Performance 418Kendall's Notation for Queuing

Systems 419Variety of Queuing Models Studied

Here 4199.4 Single-Server Queuing System with

Poisson Arrivals and Exponential ServiceTimes (M/M/l Model) 420

Assumptions of the M/M/l QueuingModel 421

Operating Characteristic Equationsfor an M/M/l Queuing System 421

Arnold's Muffler Shop Example 422Using ExcelModules for Queuing Model

Computations 422Cost Analysis of the Queuing System 426Increasing the Service Rate 426

9.5 Multiple-Server Queuing System withPoisson Arrivals and Exponential ServiceTimes (M/M/s Model) 427Operating Characteristic Equations

for an M/M/s Queuing System 428Arnold's Muffler Shop Revisited 429Cost Analysis of the Queuing System 430

9.6 Single-Server Queuing System withPoisson Arrivals and Constant ServiceTimes (MIDI 1 Model) 431

Operating Characteristic Equationsfor an M/D/l Queuing System 431

Garcia-Golding Recycling, Inc. 432Cost Analysis of the Queuing System 433

9.7 Single-Server Queuing System withPoisson Arrivals and General ServiceTimes (MIG/1 Model) 433

Operating Characteristic Equationsfor an M/G/l Queuing System 434

Meetings with Professor Crino 435Using Excel's Goal Seek to Identify Required

Model Parameters 4369.8 Multiple-Server Queuing System with

Poisson Arrivals, Exponential ServiceTimes, and Finite Population Size(M/M/s/oo/N Model) 438

Operating Characteristic Equations for theFinite Population Queuing System 438

Department of Commerce Example 439Cost Analysis of the Queuing System 440

9.9 More Complex Queuing Systems 441

Summary 442 Glossary 442 SolvedProblems 443 Discussion Questions andProblems 446 Case Study: New EnglandFoundry 450 Case Study: Winter ParkHotel 452 Bibliography 452

CHAPTER 10 Simulation Modeling 45310.1 Introduction 454

What Is Simulation? 454

Advantages and Disadvantagesof Simulation 455

10.2 Monte Carlo Simulation 457

Step 1: Establish a Probability Distributionfor Each Variable 457

Step 2: Simulate Values from the ProbabilityDistributions 458

Step 3: Repeat the Process for a Seriesof Replications 460

10.3 Role of Computers in Simulation 461

Types of Simulation Software Packages 462

Random Generation from Some CommonProbability Distributions Using Excel 462

10.4 Simulation Model to Compute ExpectedProfit 467

Setting Up the Model 468

Replication by Copying the Model 470

Replication Using a Data Table 470


70.5 Simulation Model of an InventoryProblem 473Simkin's Hardware Store 474


Computation of Costs 478

Replication Using Data Table 478


Using Scenario Manager to IncludeDecisions in a Simulation Model 480


10.6 Simulation Model of a QueuingProblem 483

Denton Savings Bank 483


Replication Using Data Table 485


10.7 Simulation Model of a RevenueManagement Problem 486

Judith's Airport Limousine Service 486


Replicating the Model Using Data Tableand Scenario Manager 489


10.8 Simulation Model of an InventoryProblem Using Crystal Ball 491

Reason for Using Add-in Programs 491

Simulation of Simkin's Hardware StoreUsing Crystal Ball 491

Xll Contents

Replicating the Model 493

Using Decision Table in Crystal Ball 495

10.9 Simulation Model of a RevenueManagement Problem Using CrystalBall 501


Using Decision Table to Identify the BestReservation Limit 502

10.10 Other Types of Simulation Models 504

Operational Gaming 504

Systems Simulation 505

Summary 505 Glossary 506 SolvedProblems 506 Discussion Questions andProblems 515 Case Study: Alabama Airlines524 Case Study: Abjar Transport Company525 Bibliography 526

CHAPTER 11 Forecasting Models 527

11.1 Introduction 528

11.2 Types of Forecasts 528

Qualitative Models 529

Time-Series Models 529

Causal Models 529

11.3 Qualitative Forecasting Models 530

11.4 Measuring Forecast Error 531

11.5 Basic Time-Series ForecastingModels 532

Components of a Time Series 532

Stationary and Nonstationary Time-Series

Data 533

Moving Averages 534Using ExcelModules for Forecasting Model

Computations 534

Weighted Moving Averages 539

Exponential Smoothing 543

/1.6 Trend and Seasonality In Time-SeriesData 547 '

Linear Trend Analysis 547

Scatter Diagram 547

Least-Squares Procedure for Developinga Linear Trend Line 550

Seasonality Analysis 554

11.7 Decomposition of a Time Series 556Multiplicative Decomposition Example:

Sawyer Piano House 556Using ExcelModules for Multiplicative

Decomposition 557

11.8 Causal Forecasting Models: Simpleand Multiple Regression 561

Causal Simple Regression Model 562

Causal Simple Regression UsingExcelModules 563

Casual Simple Regression Using Excel'sAnalysis ToolPak (Data Analysis) 568

Causal Multiple Regression Model 572

Causal Multiple Regression UsingExcelModules 573

Causal Multiple Regression Using Excel'sAnalysis ToolPak (Data Analysis) 576

Summary 581 Glossary 581 SolvedProblems 582 Discussion Questions andProblems 586 Case Study: North-SouthAirline 593 Case Study: ForecastingFootball Game Attendance at SouthwesternUniversity 594 Bibliography 595

CHAPTER 12 Inventory Control Models—appears inits entirety on Student CD-ROM

APPENDIX A Probability Conceptsand Applications 598

APPENDIX B Useful Excel Commandsand Procedures for InstallingExcelModules 625

APPENDIX C Areas Under the Standard NormalCurve 637

APPENDIX D Brief Solutions to All Odd-NumberedEnd-Of-Chapter Problems 638

INDEX 643

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DE001D461CD159BE1375DC125727A00529614