An Introduction to Management Science: Quantitative Approaches to Decision Making, 16th Edition PDF by Jeffrey D Camm, James J Cochran, Michael J Fry, Jeffrey W Ohlmann, David R Anderson, Dennis J Sweeney and Thomas A Williams

An Introduction to Management Science: Quantitative Approaches to Decision Making, 16th Edition

By Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann, David R. Anderson,

Dennis J. Sweeney and Thomas A. Williams

Contents:

Preface xvii

About the Authors xxi

Chapter 1 Introduction 1

1.1 Problem Solving and Decision Making 3

1.2 Quantitative Analysis and Decision Making 4

1.3 Quantitative Analysis 6

Model Development 7

Data Preparation 9

Model Solution 10

Report Generation 12

A Note Regarding Implementation 12

1.4 Models of Cost, Revenue, and Profit 13

Cost and Volume Models 13

Revenue and Volume Models 14

Profit and Volume Models 14

Breakeven Analysis 14

1.5 Management Science Techniques 15

Methods Used Most Frequently 16

Summary 18

Glossary 18

Problems 19

Case Problem: Scheduling a Youth Soccer League 24

Appendix 1.1: Using Excel for Breakeven Analysis 25

Chapter 2 An Introduction to Linear Programming 29

2.1 A Simple Maximization Problem 31

Problem Formulation 31

Mathematical Statement of the Par, Inc., Problem 34

2.2 Graphical Solution Procedure 35

A Note on Graphing Lines 43

Summary of the Graphical Solution Procedure

for Maximization Problems 45

Slack Variables 46

2.3 Extreme Points and the Optimal Solution 47

2.4 Computer Solution of the Par, Inc., Problem 48

Interpretation of Computer Output 49

2.5 A Simple Minimization Problem 50

Summary of the Graphical Solution Procedure

for Minimization Problems 52

Surplus Variables 53

Computer Solution of the M&D Chemicals Problem 54

2.6 Special Cases 55

Alternative Optimal Solutions 55

Infeasibility 56

Unboundedness 57

2.7 General Linear Programming Notation 59

Summary 61

Glossary 62

Problems 62

Case Problem 1: Workload Balancing 78

Case Problem 2: Production Strategy 79

Case Problem 3: Hart Venture Capital 80

Appendix 2.1: Solving Linear Programs with Excel Solver 82

Chapter 3 Linear Programming: Sensitivity Analysis and

Interpretation of Solution 87

3.1 Introduction to Sensitivity Analysis 89

3.2 Graphical Sensitivity Analysis 89

Objective Function Coefficients 90

Right-Hand Sides 94

3.3 Sensitivity Analysis: Computer Solution 97

Interpretation of Computer Output 97

Cautionary Note on the Interpretation of Dual Values 99

The Modified Par, Inc., Problem 100

3.4 Limitations of Classical Sensitivity Analysis 104

Simultaneous Changes 104

Changes in Constraint Coefficients 104

Nonintuitive Dual Values 105

3.5 The Electronic Communications Problem 109

Problem Formulation 110

Computer Solution and Interpretation 111

Summary 114

Glossary 115

Problems 116

Case Problem 1: Product Mix 136

Case Problem 2: Investment Strategy 137

Appendix 3.1: Sensitivity Analysis with Excel Solver 139

Chapter 4 Linear Programming Applications in Marketing,

Finance, and Operations Management 143

4.1 Marketing Applications 144

Media Selection 144

Marketing Research 147

4.2 Financial Applications 150

Portfolio Selection 150

Financial Planning 153

4.3 Operations Management Applications 157

A Make-or-Buy Decision 157

Production Scheduling 161

Workforce Assignment 167

Blending Problems 170

Summary 175

Problems 175

Case Problem 1: Planning an Advertising Campaign 188

Case Problem 2: Schneider’s Sweet Shop 189

Case Problem 3: Textile Mill Planning 190

Case Problem 4: Workforce Scheduling 191

Case Problem 5: Duke Energy Coal Allocation 193

Appendix 4.1: Excel Solution of Hewlitt Corporation Financial Planning

Problem 196

Chapter 5 Advanced Linear Programming Applications 201

5.1 Data Envelopment Analysis 202

Evaluating the Performance of Hospitals 202

Overview of the DEA Approach 203

DEA Linear Programming Model 204

Summary of the DEA Approach 208

5.2 Revenue Management 209

5.3 Portfolio Models and Asset Allocation 213

A Portfolio of Mutual Funds 214

Conservative Portfolio 215

Moderate Risk Portfolio 218

5.4 Game Theory 221

Competing for Market Share 221

Identifying a Pure Strategy Solution 224

Identifying a Mixed Strategy Solution 224

Summary 230

Glossary 231

Problems 231

Chapter 6 Distribution and Network Models 239

6.1 Supply Chain Models 240

Transportation Problem 240

Problem Variations 245

A General Linear Programming Model 246

Transshipment Problem 247

Problem Variations 250

A General Linear Programming Model 252

6.2 Assignment Problem 253

Problem Variations 256

A General Linear Programming Model 257

6.3 Shortest-Route Problem 258

A General Linear Programming Model 261

6.4 Maximal Flow Problem 262

6.5 A Production and Inventory Application 265

Summary 268

Glossary 269

Problems 270

Case Problem 1: Solutions Plus 286

Case Problem 2: Supply Chain Design 287

Appendix 6.1: Excel Solution of Transportation, Transshipment,

and Assignment Problems 290

Chapter 7 Integer Linear Programming 297

7.1 Types of Integer Linear Programming Models 299

7.2 Graphical and Computer Solutions for an All-Integer Linear

Program 300

Graphical Solution of the LP Relaxation 300

Rounding to Obtain an Integer Solution 301

Graphical Solution of the All-Integer Problem 302

Using the LP Relaxation to Establish Bounds 303

Computer Solution 303

7.3 Applications Involving 0-1 Variables 304

Capital Budgeting 304

Fixed Cost 305

Distribution System Design 307

Bank Location 310

Product Design and Market Share Optimization 314

7.4 Modeling Flexibility Provided by 0-1 Integer Variables 318

Multiple-Choice and Mutually Exclusive Constraints 318

k out of n Alternatives Constraint 318

Conditional and Corequisite Constraints 319

A Cautionary Note About Sensitivity Analysis 321

Summary 322

Glossary 322

Problems 323

Case Problem 1: Textbook Publishing 336

Case Problem 2: Yeager National Bank 337

Case Problem 3: Production Scheduling with Changeover Costs 338

Case Problem 4: Applecore Children’s Clothing 339

Appendix 7.1: Excel Solution of Integer Linear Programs 341

Chapter 8 Nonlinear Optimization Models 345

8.1 A Production Application—Par, Inc., Revisited 347

An Unconstrained Problem 347

A Constrained Problem 348

Local and Global Optima 350

Sensitivity Analysis 353

8.2 Constructing an Index Fund 354

8.3 Markowitz Portfolio Model 358

8.4 Blending: The Pooling Problem 360

8.5 Forecasting Adoption of a New Product 365

Summary 370

Glossary 370

Problems 371

Case Problem 1: Portfolio Optimization with Transaction Costs 379

Case Problem 2: Cafe Compliance in the Auto Industry 382

Appendix 8.1: Solving Nonlinear Optimization Problems

with Excel Solver 385

Chapter 9 Project Scheduling: PERT/CPM 389

9.1 Project Scheduling Based on Expected Activity Times 390

The Concept of a Critical Path 391

Determining the Critical Path 393

Contributions of PERT/CPM 397

Summary of the PERT/CPM Critical Path Procedure 398

9.2 Project Scheduling Considering Uncertain Activity Times 399

The Daugherty Porta-Vac Project 399

Uncertain Activity Times 399

The Critical Path 402

Variability in Project Completion Time 403

9.3 Considering Time–Cost Trade-Offs 407

Crashing Activity Times 408

Linear Programming Model for Crashing 410

Summary 412

Glossary 412

Problems 413

Case Problem 1: R. C. Coleman 423

Appendix 9.1: Finding Cumulative Probabilities for Normally

Distributed Random Variables 425

Chapter 10 Inventory Models 427

10.1 E conomic Order Quantity (EOQ) Model 428

The How-Much-to-Order Decision 432

The When-to-Order Decision 433

Sensitivity Analysis for the EOQ Model 434

Excel Solution of the EOQ Model 435

Summary of the EOQ Model Assumptions 436

10.2 E conomic Production Lot Size Model 437

Total Cost Model 437

Economic Production Lot Size 439

10.3 Inventory Model with Planned Shortages 440

10.4 Quantity Discounts for the EOQ Model 444

10.5 Single-Period Inventory Model with Probabilistic Demand 447

Neiman Marcus 447

Nationwide Car Rental 450

10.6 Order-Quantity, Reorder Point Model with Probabilistic Demand 451

The How-Much-to-Order Decision 453

The When-to-Order Decision 453

10.7 Periodic Review Model with Probabilistic Demand 455

More Complex Periodic Review Models 458

Summary 459

Glossary 459

Problems 460

Case Problem 1: Wagner Fabricating Company 468

Case Problem 2: River City Fire Department 469

Appendix 10.1: Development of the Optimal Order Quantity (Q)

Formula for the EOQ Model 471

Appendix 10.2: Development of the Optimal Lot Size (Q*) Formula for

the Production Lot Size Model 471

Chapter 11 Waiting Line Models 473

11.1 Structure of a Waiting Line System 475

Single-Server Waiting Line 475

Distribution of Arrivals 475

Distribution of Service Times 477

Queue Discipline 477

Steady-State Operation 478

11.2 Single-Server Waiting Line Model with Poisson Arrivals and

Exponential Service Times 478

Operating Characteristics 478

Operating Characteristics for the Burger Dome Problem 479

Managers’ Use of Waiting Line Models 480

Improving the Waiting Line Operation 480

Excel Solution of Waiting Line Model 481

11.3 Multiple-Server Waiting Line Model with Poisson Arrivals and

Exponential Service Times 482

Operating Characteristics 483

Operating Characteristics for the Burger Dome Problem 484

11.4 Some General Relationships for Waiting Line Models 487

11.5 Economic Analysis of Waiting Lines 488

11.6 Kendall’s Notation for Classifying Queueing Models 490

11.7 Single-Server Waiting Line Model with Poisson Arrivals and General

Service Times 491

Operating Characteristics for the M/G/1 Model 491

Constant Service Times 492

11.8 Multiple-Server Model with Poisson Arrivals, General Service Times,

and No Waiting Line 493

Operating Characteristics for the M/G/k Model with Blocked

Customers Cleared 493

11.9 Waiting Line Models with Finite Calling Populations 495

Operating Characteristics for the M/M/1 Model with a

Finite Calling Population 495

Summary 498

Glossary 499

Problems 499

Case Problem 1: Regional Airlines 508

Case Problem 2: Olympus Equipment, Inc. 509

Chapter 12 Simulation 511

12.1 What-If Analysis 513

Sanotronics 513

Base-Case Scenario 513

Worst-Case Scenario 514

Best-Case Scenario 514

12.2 Simulation of Sanotronics Problem 515

Use of Probability Distributions to Represent Random Variables 515

Generating Values for Random Variables with Excel 516

Executing Simulation Trials with Excel 520

Measuring and Analyzing Simulation Output 522

12.3 Inventory Simulation 524

Simulation of the Butler Inventory Problem 526

12.4 Waiting Line Simulation 529

Black Sheep Scarves 530

Customer (Scarf) Arrival Times 530

Customer (Scarf) Service (Inspection) Times 531

Simulation Model 531

Simulation of Black Sheep Scarves 534

Simulation with Two Quality Inspectors 535

Simulation Results with Two Quality Inspectors 537

12.5 Simulation Considerations 538

Verification and Validation 538

Advantages and Disadvantages of Using Simulation 539

Summary 539

Summary of Steps for Conducting a Simulation Analysis 540

Glossary 540

Problems 541

Case Problem 1: Four Corners 549

Case Problem 2: Harbor Dunes Golf Course 550

Case Problem 3: County Beverage Drive-Thru 552

Appendix 12.1: Common Probability Distributions for Simulation 554

Chapter 13 Decision Analysis 561

13.1 Problem Formulation 563

Influence Diagrams 563

Payoff Tables 564

Decision Trees 564

13.2 Decision Making Without Probabilities 565

Optimistic Approach 566

Conservative Approach 566

Minimax Regret Approach 567

13.3 Decision Making with Probabilities 568

Expected Value of Perfect Information 571

13.4 Risk Analysis and Sensitivity Analysis 572

Risk Analysis 572

Sensitivity Analysis 573

13.5 Decision Analysis with Sample Information 577

Influence Diagram 577

Decision Tree 578

Decision Strategy 580

Risk Profile 582

Expected Value of Sample Information 586

Efficiency of Sample Information 586

13.6 Computing Branch Probabilities with Bayes’ Theorem 586

13.7 Utility Theory 590

Utility and Decision Analysis 592

Utility Functions 595

Exponential Utility Function 598

Summary 600

Glossary 600

Problems 602

Case Problem 1: Property Purchase Strategy 617

Case Problem 2: Lawsuit Defense Strategy 618

Case Problem 3: Rob’s Market 619

Case Problem 4: College Softball Recruiting 620

Chapter 14 Multicriteria Decisions 623

14.1 Goal Programming: Formulation and Graphical Solution 624

Developing the Constraints and the Goal Equations 626

Developing an Objective Function with Preemptive Priorities 627

Graphical Solution Procedure 628

Goal Programming Model 631

14.2 Goal Programming: Solving More Complex Problems 632

Pérez Office Supplies Problem 632

Formulating the Goal Equations 633

Formulating the Objective Function 634

Computer Solution 635

14.3 Scoring Models 637

14.4 Analytic Hierarchy Process 640

Developing the Hierarchy 641

14.5 Establishing Priorities Using AHP 642

Pairwise Comparisons 642

Pairwise Comparison Matrix 644

Synthesization 645

Consistency 646

Other Pairwise Comparisons for the Car Selection Problem 648

14.6 Using AHP to Develop an Overall Priority Ranking 649

Summary 651

Glossary 652

Problems 652

Case Problem 1: Banh Trailers, Inc. 662

Appendix 14.1: Scoring Models with Excel 663

Chapter 15 Time Series Analysis and Forecasting 665

15.1 Time Series Patterns 667

Horizontal Pattern 667

Seasonal Pattern 671

Trend and Seasonal Pattern 671

Cyclical Pattern 672

Selecting a Forecasting Method 674

15.2 Forecast Accuracy 675

15.3 Moving Averages and Exponential Smoothing 679

Moving Averages 679

Weighted Moving Averages 682

Exponential Smoothing 683

15.4 Linear Trend Projection 686

15.5 Seasonality 690

Seasonality Without Trend 690

Seasonality with Trend 693

Models Based on Monthly Data 695

Summary 696

Glossary 696

Problems 697

Case Problem 1: Forecasting Food and Beverage Sales 704

Case Problem 2: Forecasting Lost Sales 705

Appendix 15.1: Forecasting with Excel Data Analysis Tools 707

Appendix 15.2: Using the Excel Forecast Sheet 716

Chapter 16 Markov Processes 723

16.1 Market Share Analysis 724

16.2 Accounts Receivable Analysis 732

Fundamental Matrix and Associated Calculations 733

Establishing the Allowance for Doubtful Accounts 734

Summary 736

Glossary 737

Problems 737

Case Problem 1: Dealer’s Absorbing State Probabilities in

Blackjack 742

Appendix 16.1: Matrix Notation and Operations 744

Appendix 16.2: Matrix Inversion with Excel 747

Appendices 749

Appendix A Building Spreadsheet Models 750

Appendix B Areas for the Standard Normal Distribution 779

Appendix C Values of e2l 781

Appendix D References and Bibliography 782

Appendix E Solutions to Even-Numbered Exercises (MindTap Reader)

Index 784

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