管理科学导论 英文版·第8版PDF电子书下载

ContentsCHAPTER ONE Introduction 1

F Answers to Even-Numbered Problems F- 1

E References and Bibliography E- 1

D Matnx Notation and Operations D- 1

C Values of e-λC- 1

B Random Digits B- 1

Appendixes A- 1

G Solutions to Self-Test Problems G- 1

A Areas for the Standard Normal Distribution A- 2

1.1 Problem Sotving and Decision Making 2

1.2 Quantitative Analvsis and Decision Making 3

1.3 Guantitative Analysis 5

Model Development 6

Data Preparation 9

Model Solution 10

Report Generation 11

A Note Regarding Implementation 11

Revenue and Volume Models 12

Cost and Volume Models 12

1.4 Models of Cost,Revenue,and Profit 12

Profit and Volume Models 13

Break-Even Analysis 13

1.5 Management Science in Practice 14

Management Science Techniques 14

Methods Used Most Frequently 15

Glossary 17

Summary 17

Problems 18

Appendix 1.1 Spreadsheets for Management Science 21

Appendix 1.2 The Management Scientist Software Package 22

Management Science in Practice:Mead Corporation 25

CHAPTER TWO Linear Programming:The Graphical Method 27

2.1 A Simple Maximization Problem 28

The Objective Function 29

The Constraints 30

Mathematical Statement of the Par,Inc.,Problem 31

2.2 Graphical Solution 32

A Note on Graphing Lines 41

Summary of the Graphical Solution Procedure for Maximization Problems 43

Slack Variables 43

2.3 Extreme Points and the Optimal Solution 45

2.4 A Simple Minimization Problem 47

Surplus Variables 50

Summary of the Graphical Solution Procedure for Minimization Problems 50

Alternative Optimal Solutions 52

2.5 Special Cases 52

Infeasibility 53

Unbounded 54

2.6 Introduction to Sensitivity Analysis 56

Obiective Function Coefficients 57

2.7 Graphical Sensitivity Analysis 57

Right-Hand Sides 62

Glossary 64

Summary 64

Problems 65

Case Problem:Production Strategy 79

Case Problem:Advertising Strategy 79

CHAPTER THREE Linear Programming:Formulation,Computer Solution,and Interpretation 81

3.1 Computer Solution of Linear Programs 81

Interpretation of Computer Output 82

Simultaneous Changes 85

Interpretation of Computer Output—A Second Example 87

Cautionary Note on the Interpretation of Dual Prices 89

3.2 More Than Two Decision Variables 90

The Modified Par,Inc.,Problem 90

The Bluegrass Farms Problem 95

Formulation of the Bluegrass Farms Problem 95

Computer Solution and Interpretation for the Bluegrass Farms Problem 97

Guidelines for Model Formulation 99

3.3 Modeling 99

Management Science in Action:An Optimal Wood Procurement Policy 100

The Electronic Communications Problem 101

Formulation of the Electronic Communications Problem 102

Computer Solution and Interpretation for the Electronic Communications Problem 103

Management Science in Action:Using Linear Programming for Traffic Control 106

Glossary 107

Summary 107

Problems 108

case Problem:Product Mix 121

Case Problem:Truck Leasing Strategy 122

Appendix 3.2:Solving Linear Programs with LINDO/PC 123

Appendix 3.1:Solving Linear Programs with The Management Scientist 123

Appendix 3.3:Spreadsheet Solution of Linear Programs 126

Management Science in Practice:Eastman Kodak 130

CHAPTER FOUR Linear Programming Applications 132

4.1 Marketing Applications 132

Media Selection 132

Marketing Research 136

Portfolio Selection 139

4.2 Financial Applications 139

Financial Planning 143

Management Science in Action:Using Linear Programming for Optimal Lease Structuring 143

A Make-or-Buy Decision 147

4.3 Production Management Applications 147

Production Scheduling 150

Management Science in Action:Libbey-Owens-Ford 157

Work-Force Assignment 157

4.4 Blending Problems 161

Evaluating the Performance of Hospitals 166

4.5 Data Envelopment Analvsis 166

An Overview of the DEA Approach 167

The DEA Linear Programming Model 168

Summary of the DEA Approach 172

Summary 173

Problems 174

Case Problem:Environmental Protection 187

Case Problem:Investment Strategy 189

Case Problem:Textile Mill Scheduling 190

Appendix 4.1 Spreadsheet Solution of Linear Programs 191

Management Science in Practice:Marathon Oil Company 193

CHAPTET FIVE Linear Programming:The Simplex Method 195

5.1 An Algebraic Overview of the Simplex Method 195

Management Science in Action:Fleet Assignment at Delta Air Lines 196

Algebraic Properties of the Simplex Method 197

Determining a Basic Solution 197

5.2 Tableau Form 198

Basic Feasible Solutions 198

5.3 Setting Up the Initial Simplex Tableau 200

5.4 Improving the Solution 203

5.5 Calculating the Next Tableau 205

Interpreting the Results of an Iteration 207

Moving toward a Better Solution 208

Interpreting the Optimal Solution 210

Summary of the Simplex Method 211

5.6 Tableau Form:The General Case 212

Greater-Than-or-Equal-to Constraints 213

Equality Constraints 217

Eliminating Negative Right-Hand-Side Values 217

Summary of the Steps to Create Tableau Form 218

5.7 Solving a Minimization Problem 220

5.8 Special Cases 222

Infeasibility 222

Unboundedness 223

Altemative Optimal Solutions 224

Degeneracy 226

Summary 227

Problems 229

Glossary 229

Objective Function Coefficients 239

6.1 Sensitivity Analysis with the Simplexrableau 239

CHAPTER SIX Simplex-Based Sensitivity Analysis and Duality 239

Right-Haod-Side Values 244

Simultaneous Changes 250

6.2 Duality 251

Economic Interpretation of the Dual Variables 254

Using the Dual to Identify the Primal Solution 255

Findingthe Dual of Anv Primal Problem 256

Glossary 258

Summary 258

Problems 259

Management Science in Practice:Performance Analysis Corporation 266

CHAPTER　SEVEN　Transportation,Assignment,and Transshipment Problems 268

7.1 The Transportation Problem:The Network Model and a Linear Programming Formulation 268

Problem Variations 273

A General Linear Programming Model of the Transportation Problem 274

7.2 The Assignment Problem:The Network Model and a Linear Programming Formulation 276

Management Science in Action:Marine Corps Mobilization 276

Problem Variations 279

Multiple Assignments 280

A General Linear Programming Model of the Assignment Problem 280

7.3 The Transshipment Problem:The Nelwork Model and a Linear Programming Formulation 281

Problem Variations 286

A General Linear Programming Model of the Transshipment Problem 287

7.4 A Production and Inventory Application 288

7.5 The Transportation Simplex Method:A Special-Purpose Solution Procedure(Optional) 291

Phase Ⅰ:Finding an Initial Feasible Solution 292

Phase Ⅱ:Iterating to the Optimal Solution 296

Summary of the Transportation Simplex Method 305

Problem Variations 306

7.6 The Assignment Problem:A Special-Purpose Solution Procedure(Optional) 307

Finding the Minimum Number of Lines 310

Problem Variations 310

Summary 313

Glossary 314

Problems 315

Case Problem:Assigning Umpire Crews 330

Case Problem:Distribution System Design 332

Management Science jn Practice:Procter Gamble 334

CHAPTER EIGHT Integer Linear Programming 335

Management Science in Action:Scheduling Employees at McDonald's Restaurant 336

8.1 Types of Integer Linear Programming Models 336

8.2 Graphicaland Computer Solution for an All-Integer Linear Program 338

Graphical Solution Procedure 338

Computer Solution 341

Management Science in Action:Cutting Photographic Color Paper Rolls 341

8.3 Applications 342

Capital Budgeting 342

Models Involving Fixed Costs 344

Distribution System Design 346

ABank Location Application 350

8.4 Modeling Flexibility Provided by 0-1 Integer Variables 354

Multiple-Choice and Mutually Exclusive Constraints 355

Management Science in Action:Analyzing Price Quotations Under Business Volume Discounts 355

k Out of n Alternatives Constraint 356

Conditional and Corequisite Constraints 356

Summary 357

A Cautionary Note on Sensitivity Analysis 357

Problems 358

Glossary 358

Case Problem:Textbook Publishing 367

Case Problem:Production Scheduling with Changeover Costs 369

Management Science in Practice:Ketron 370

CHAPTER NINE Network Models 372

9.1 The Shortest-Route Problem 372

A Shortest-Route Algorithm 373

A Minimal Spanning Tree Algorithm 381

9.2 The Minimal Spanning Tree Problem 381

9.3 The Maximal Flow Problem 384

A Maximal Flow Algorithm 385

Glossary 390

Problems 390

Summary 390

Case Problem:Ambulance Routing 397

Management Science in Practice:EDS 399

CHAPTER TEN Project Scheduling:PERT/CPM 401

10.1 Project Scheduling with Known Activity Times 402

The Concepts of a Critical Path 403

Determining the Critical Path 404

Contributions of PERT/CPM 409

Management Science in Action:Project Management on the PC 410

Summary of the PERT/CPM Critical Path Procedure 411

The Daugherty Porta-Vac Project 412

10.2 Project Scheduling with Uncertain Activity Times 412

Uncertain Activity Times 413

The Critical Path 415

Variability in Project Completion Time 418

10.3 Considering Time-Cost Trade-Offs 420

Crashing Activity Times 421

A Linear Programming Model for Crashing Decisions 423

Summary 425

Glossary 425

Problems 426

Case Problem:Warehouse Expansion 435

Management Science in Practice:Seasongood Mayer 436

CHAPTER ELEVEN Inventory Models 439

11.1 Economic Order Quantity(EOQ)Model　 440

The How-Much-to-Order Decision 443

The When-to-Order Decision 445

Sensitivity Analysis in the EOQ Model 446

The Manager's Use of the EOQ Model 446

A Summary of the EOQ Model Assumptions 447

How Has the EOQ Decision Model Helped? 447

11.2 Economic Production Lot Size Model 448

The Total Cost Model 449

Finding the Economic Production Lot Size 451

11.4 Quantity Discounts for the EOQ Model 452

11.3 An Inventory Model with Planned Shortages 452

11.5 A Single-Period lnventory Model with Probabilistic Demand 458

The Johnson Shoe Company Problem 459

The Kremer Chemical Company Problem 462

11.6 An Order-Quantity,Reorder-Point Model with Probabilistic Demand 464

The When-to-Order Decision 466

The How-Much-to-Order Decision 466

11.7 A Periodic-Review Model with Probabilistic Demand 468

Management Science in Action:Information from a Netherlands Supplier Lowers Inventory Cost 468

More Complex Periodic-Review Models 471

Management Science in Action:Inventory Model Helps Hewlett-Packard's Product Design for Worldwide Markets 471

11.8 Material Requirements Planning 472

Dependent Demand and the MRP Concept 473

Information System for MRP 474

MRP Calculations 476

11.9 The Just-in-Time Approach to Inventory Management 478

Summary 479

Glossary 479

Problems 481

Case Problem:A Make-or-Buy Analysis 485

Appendix 11.1:Inventory Models with Spreadsheets 489

Appendix 11.3 Development of the Optimal Lot Size(Q)Formula for the Production Lot Size Model 492

Appendix 11.2 Development of the Optimal Order-Quantity(Q)Formula for the EOQ Model 492

Appendix 11.4 Development of the Optimal Order-Quantity(Q)and Optimal Backorder(S)Formulas for the Planned Shortage Model 493

Management Science in Practice:SupeRx.Inc. 495

CHAPTER TWELVE Waiting Line Models 497

12.1 The Structure of a Waiting Line System 498

The Single-Channel Waiting Line 498

The Distribution of Arrivals 498

The Distribution of Service Times 499

Queue Discipline 500

Steady-State Operation 500

The Operating Characteristics 501

12.2 The Single-Channel Waiting Line Model with Poisson Arrivals and Exponential Service Times 501

Operating Characteristics for the Burger Dome Problem 502

The Manager's Use of Waiting Line Models 503

Improving the Waiting Line Operation 503

12.3 The Multiple-Channel Waiting Line Model with Poisson Arrivals and Exponential Service Times 504

The Operatinig Characteristics 505

Operating Characteristics for the Burger Dome Problem 506

Management Science in Action:Hospital Staffing Based on a Multiple-Channel Waiting Line Model 509

12.4 Some General Relationships for Waiting Line Models 509

12.5 Economic Analysis of Waiting Lines 511

12.6 Other Waiting Line Models 513

Operating Characteristics for the M/G/l Model 514

12.7 The Single-Channel Waiting Line Model with Poisson Arrivals and Arbitrary Service Times 514

Constant Service Times 515

12.8 A Multiple-Channel Model with Poisson Arrivals,Arbitrary Service Times,and No Waiting Line 516

The Operating Characteristics for the M/G/K Model with Blocked Customers Cleared 517

The Operating Characteristics for the M/M/l Model with a Finite Calling Population 519

12.9 Waiting Line Models with Finite Calling Populations 519

Summary 522

Management Science in Action:Improving Fire Department Productivity 522

Glossary 523

Problems 523

Case Problem:Airline Reservations 530

Appendix 12.1:Waiting Line Models with Spreadsheets 531

Management Science in Practice:CITIBANK 533

CHAPER THIRETTEN Simulation 535

13.1 Using Simulation for Risk Analysis 536

The PortaCom Project 536

The PortaCom Simulation Model 537

Random Numbers and Simulating Values of Random Variables 539

Using the Simulation Model 541

Risk Analysis Conclusions 542

Simulation Results 542

13.2 An Inventory Simulation Model 543

Some Simulation Terminology 543

13.3 A Waiting Line Simulation Model 546

The Hammondsport Savings and Loan Waiting Line 546

Customer Arrival Times 546

Customer Service Times 547

The Simulation Model 548

Simulation Results 551

Management Science in Action:Red Cross Uses Simulation to Improve Bloodmobile Services 553

Selecting a Simulation Language 554

13.4 Other lssues 554

Verification and Validation 555

Keeping Track of Time 556

Advantages and Disadvantages 556

Management Science in Action:Simulation at Mexico's Vilpac Truck Company 557

Summary 557

Glossary 558

Problems 558

Case Problem:County Beverage Drive-Thru 567

Case Problem:Machine Repair 568

Appendix 13.1 Simulation with Spreadsheets 569

Management Science in Practice:The Upjohn Company 573

CHAPTER FOURTEEN Decision Analysis 575

Payoff Tables 576

14.1 Structuring the Decision Problem 576

Decision Trees 577

14.2 Decision Making Without Probabilities 578

Optimistic Approach 579

Conservative Approach 579

Minimax Regret Approach 580

14.3 Decision Making with Probabilities 581

14.4 Sensitivity Analvsis 584

Management Science in Action:Decision Analysis and the Selection of Home Mortgages 584

14.5 Expected Value of Perfect Information 587

14.6 Decision Analvsis with Sample Information 589

14.7 Developing a Decision Strategy 591

Computing Branch Probabilities 592

An Optimal Decision Strategy 594

Management Science in Action:Decision Analysis and Drug Testing for Student Athletes 596

14.8 Expected Value of Sample Information 597

Efficiency of Sample Information 598

The Meaning of Utility 599

14.9 Utility and Decision Making 599

Developing Utilities for Payoffs 600

The Expected Utility Approach 603

Glossary 605

Summary 605

Problems 606

Case Problem:Property Purchase Strategy 622

Appendix 14.1:Decision Analysis and Spreadsheets 623

Management Science in Practice:Ohio Edison Company 627

CHAPTET FIFTEEN Multicriteria Decision Problems 630

15.1 Goal Programming:Formulation and Graphical Solution 631

Developing the Constraints and the Goal Equations 632

Developing an Objective Function with Preemptive Priorities 633

The Graphical Solution Procedure 634

The Goal Programming Model 638

15.2 Goal Programming:Solving More Complex Problems 639

The Suncoast Office Supplies Problem 639

Formulating the Goal Equations 640

Formulating the Objective Function 641

Computer Solution 643

15.3 The Analytic Hierarchy Process 646

Management Science in Action:Using AHP and Goal Programming to Plan Facility Locations 647

Developing the Hierarchy 648

15.4 Establishing Priorities Using AHP 648

The Pairwise Comparison Matrix 649

Pairwise Comparisons 649

Procedure for Synthesizing Judgments 650

Synthesis 650

Consistency 651

Estimating the Consistency Ratio 652

Other Pairwise Comparisons for the Car-Selection Problem 653

15.5 Using AHP to Develop an Overall Priority Ranking 655

15.6 Using Expert Choice to Implement AHP 656

Summary 659

Glossary 660

Problems 661

Case Problem:Production Scheduling 668

CHAPTER XIXTEEN Forecasting 669

16.1 The Components of a Time Series 670

Trend Component 671

Cyclical Component 672

Seasonal Component 672

Irregular Component 673

16.2 Smoothing Methods 673

Moving Averages 673

Weighted Moving Averages 676

Exponential Smoothing 676

16.3 Trend Projection 681

16.4 Trend and Seasonal Components 684

The Multiplicative Model 685

Calculating the Seasonal Indexes 685

Deseasonalizing the Time Series 689

Using the Deseasonalized Time Series to Identify Trend 690

Seasonal Adjustments 692

Models Based on Monthy Data 692

Cyclical Component 692

16.5 Forecasting Using Regression Models 693

Management Science in Action:Spare Parts Forecasting at American Airlines 693

Using Regression Analysis When Time Series Data Are Not Available 694

Using Regression Analysis with Time Series Data 698

Delphi Method 700

16.6 Qualitative Approaches to Forecasting 700

Scenario Writing 701

Expert Judgment 701

Management Science in Action:The Business Week Industry Outlook 70lIntuitive Approaches 702

Summary 702

Glossary 702

Problems 703

Case Problem:Forecasting Sales 712

Case Problem:Forecasting Lost Sales 713

Appendix 16.1 Forecasting with Spreadsheets 714

Management Science in Practice:The Cincinnati Gas Electric Company 716

CHAPTER SEVENTEEN Markov Processes 718

17.1 Market Share Analysis 718

17.2 Accounts Receivable Analysis 726

The Fundamental Matrix and Associated Calculations 727

Establishing the Allowance for Doubtful Accounts 729

Problems 731

Glossary 731

Summary 731

Management Science in Practice:U.S.General Accounting Office 735

18.1 A Shortest-Route Problem 737

CHAPTER ELGHTEEN Dynamic Programming 737

18.2 Dynamic Programming Notation 742

18.3 The Knapsack Problem 745

18.4 A Production and Inventory Control Problem 751

Summary 755

Glossary 755

Problems 756

Management Science in Practice:The U.S.Environmental Protection Agency 762