生产与运作分析 第六版 英文PDF电子书下载

Chapter 1 Strategy and Competition 1

Chapter Overview 1

Snapshot Application：Apple Adopts a New Business Strategy and Shifts Its Core Competency from Computers to Portable Music 3

1.1 Manufacturing Matters 5

Manufacturing Jobs Outlook 6

1.2 A Framework for Operations Strategy 7

Strategic Dimensions 8

1.3 The Classical View of Operations Strategy 9

Time Horizon 9

Focus 11

Evaluation 12

Consistency 12

1.4 Competing in the Global Marketplace 14

Problems for Sections 1.1-1.4 16

Snapshot Application：Global Manufacturing Strategies in the Automobile Industry 17

1.5 Strategic Initiatives：Reengineering the Business Process 18

1.6 Strategic Initiatives：Just-in-Time 21

1.7 Strategic Initiatives：Time-Based Competition 23

1.8 Strategic Initiatives：Competing on Quality 24

Problems for Sections 1.5-1.8 26

1.9 Matching Process and Product Life Cycles 27

The Product Life Cycle 27

The Process Life Cycle 28

The Product-Process Matrix 29

Problems for Section 1.9 31

1.10 Learning and Experience Curves 31

Learning Curves 32

Experience Curves 34

Learning and Experience Curves and Manufacturing Strategy 36

Problems for Section 1.10 36

1.11 Capacity Growth Planning：A Long-Term Strategic Problem 38

Economies of Scale and Economies of Scope 38

Make or Buy：A Prototype Capacity Expansion Problem 39

Dynamic Capacity Expansion Policy 40

Issues in Plant Location 44

Problems for Section 1.11 46

1.12 Summary 47

Additional Problems for Chapter 1 48

Appendix 1-A Present Worth Calculations 50

Bibliography 51

Chapter 2 Forecasting 52

Chapter Overview 52

2.1 The Time Horizon in Forecasting 55

2.2 Characteristics of Forecasts 56

2.3 Subjective Forecasting Methods 56

2.4 Objective Forecasting Methods 57

Causal Models 57

Time Series Methods 58

Snapshot Application：Advanced Forecasting，Inc.，Serves the Semiconductor Industry 59

Problems for Sections 2.1-2.4 59

2.5 Notation Conventions 61

2.6 Evaluating Forecasts 61

Problems for Section 2.6 63

2.7 Methods for Forecasting Stationary Series 64

MovingAverages 64

Problems on Moving Averages 67

Exponential Smoothing 67

Multiple-Step-Ahead Forecasts 71

Comparison of Exponential Smoothing and MovingAverages 72

Problems for Section 2.7 73

Snapshot Application：Sport Obermeyer Slashes Costs with Improved Forecasting 74

2.8 Trend-Based Methods 75

Regression Analysis 75

Problems for Section 2.8 76

Double Exponential Smoothing Using Holt’s Method 77

More Problems for Section 2.8 78

2.9 Methods for Seasonal Series 79

Seasonal Factors for Stationary Series 79

Seasonal Decomposition Using Moving Averages 81

Problems for Section 2.9 83

Winters ’s Method for Seasonal Problems 84

More Problems for Section 2.9 89

2.10 Box-Jenkins Models 89

Estimating the Autocorrelation Function 90

The Autoregressive Process 93

The Moving-Average Process 94

Mixtures：ARMA Models 96

ARIMA Models 96

Using ARIMA Models for Forecasting 98

Summary of the Steps Required for Building ARIMA Models 99

Case Study：Using Box-Jenkins Methodology to Predict Monthly International Airline Passenger Totals 100

SnapshotApplication：A Simple ARIMA Model Predicts the Performance of the U.S.Economy 104

Box-Jenkins Modeling—A Critique 104

Problems for Section 2.10 104

2.11 Practical Considerations 105

Model Identification and Monitoring 105

Simple versus Complex Time Series Methods 106

2.12 Overview of Advanced Topics in Forecasting 107

Simulation as a Forecasting Tool 107

Forecasting Demand in the Presence of Lost Sales 108

2.13 Linking Forecasting and Inventory Management 110

Snapshot Application：Predicting Economic Recessions 111

2.14 Historical Notes and Additional Topics 112

2.15 Summary 113

Additional Problems on Forecasting 113

Appendix 2-A Forecast Errors for Moving Averages and Exponential Smoothing 118

Appendix 2-B Derivation of the Equations for the Slope and Intercept for Regression Analysis 120

Appendix 2-C Glossary of Notation for Chapter 2 122

Bibliography 122

Chapter 3 Aggregate Planning 124

Chapter Overview 124

3.1 Aggregate Units of Production 127

3.2 Overview of the Aggregate Planning Problem 128

3.3 Costs in Aggregate Planning 130

Problems for Sections 3.1-3.3 132

3.4 A Prototype Problem 133

Evaluation of a Chase Strategy（Zero Inventory Plan） 135

Evaluation of the Constant Workforce Plan 136

Mixed Strategies and Additional Constraints 138

Problems for Section 3.4 139

3.5 Solution of Aggregate Planning Problems by Linear Programming 141

Cost Parameters and Given Information 141

Problem Variables 142

Problem Constraints 142

Rounding the Variables 143

Extensions 144

Other Solution Methods 146

3.6 Solving Aggregate Planning Problems by Linear Programming：An Example 147

Problems for Sections 3.5 and 3.6 149

3.7 The Linear Decision Rule 152

3.8 Modeling Management Behavior 153

Problems for Sections 3.7 and 3.8 155

3.9 Disaggregating Aggregate Plans 155

Snapshot Application：Welch’s Uses Aggregate Planning for Production Scheduling 157

Problems for Section 3.9 158

3.10 Production Planning on a Global Scale 158

3.11 Practical Considerations 159

3.12 Historical Notes 160

3.13 Summary 161

Additional Problems on Aggregate Planning 162

Appendix 3-A Glossary of Notation for Chapter 3 167

Bibliography 168

Supplement 1 Linear Programming 169

S1.1 Introduction 169

S1.2 A Protorype Linear Programming Problem 169

S1.3 Statement of the General Problem 171

Definitions of Commonly Used Terms 172

Features of Linear Programs 173

S1.4 Solving Linear Programming Problems Graphically 174

Graphing Linear Inequalities 174

Graphing the Feasible Region 176

Finding the Optimal Solution 177

Identifying the Optimal Solution Directly by Graphical Means 179

S1.5 The Simplex Method：An Overview 180

S1.6 Solving Linear Programming Problems with Excel 181

Entering Large Problems Efficiently 185

S1.7 Interpreting the Sensitivity Report 187

Shadow Prices 187

Objective Function Coefficients and Right-Hand Sides 188

Adding a New Variable 188

Using Sensitivity Analysis 189

S1.8 Recognizing Special Problems 191

Unbounded Solutions 191

Empty Feasible Region 192

Degeneracy 194

Multiple Optimal Solutions 194

Redundant Constraints 194

S1.9 The Application of Linear Programming to Production and Operations Analysis 195

Bibliography 197

Chapter 4 Inventory Control Subject to Known Demand 198

Chapter Overview 198

4.1 Types of Inventories 201

4.2 Motivation for Holding Inventories 202

4.3 Characteristics of Inventory Systems 203

4.4 Relevant Costs 204

Holding Cost 204

Order Cost 206

Penalty Cost 207

Problems for Sections 4.1-4.4 208

4.5 The EOQ Model 210

The Basic Model 210

Inclusion of Order Lead Time 213

Sensitivity 214

EOQ and JIT 215

Problems for Section 4.5 216

4.6 Extension to a Finite Production Rate 218

Problems for Section 4.6 219

4.7 Quantity Discount Models 220

OptimalPolicy for All-Units Discount Schedule 221

Summary of the Solution Technique for All-Units Discounts 223

Incremental Quantity Discounts 223

Summary of the Solution Technique for Incremental Discounts 225

Other Discount Schedules 225

Problems for Section 4.7 226

4.8 Resource-Constrained Multiple Product Systems 227

Problems for Section 4.8 230

4.9 EOQ Models for Production Planning 230

Problems for Section 4.9 234

4.10 Power-of-Two Policies 235

4.11 Historical Notes and Additional Topics 237

Snapshot Application：Mervyn’s Recognized for State-of-the-Art Inventory Control System 238

4.12 Summary 239

Additional Problems on Deterministic Inventory Models 240

Appendix 4-A Mathematical Derivations for Multiproduct Constrained EOQ Systems 244

Appendix 4-B Glossary of Notation for Chapter 4 246

Bibliography 246

Chapter 5 Inventory Control Subject to Uncertain Demand 248

Chapter Overview 248

Overview of Models Treated in This Chapter 252

5.1 The Nature of Randomness 253

5.2 Optimization Criterion 255

Problems for Sections 5.1 and 5.2 256

5.3 The Newsboy Model 257

Notation 257

Development of the Cost Function 258

Determining the Optimal Policy 259

Optimal Policy for Discrete Demand 261

Extension to Include Starting Inventory 261

Snapshot Application：Using Inventory Models to Manage the Seed-Corn Supply Chain at Syngenta 262

Extension to Multiple Planning Periods 263

Problems for Section 5.3 264

5.4 Lot Size-Reorder Point Systems 266

Describing Demand 267

Decision Variables 267

Derivation of the Expected Cost Function 267

The Cost Function 269

Inventory Level versus Inventory Position 271

5.5 Service Levels in（Q，R）Systems 272

Type 1 Service 272

Type 2 Service 273

Optimal（Q，R）Policies Subject to Type 2 Constraint 274

Imputed Shortage Cost 275

Scaling of Lead Time Demand 276

Estimating Sigma When Inventory Control and Forecasting Are Linked 276

Lead Time Variability 277

Calculations in Excel 278

Negative Safety Stock 278

Problems for Sections 5.4 and 5.5 279

5.6 Additional Discussion of Periodic-Review Systems 281

（s，S）Policies 281

Service Levels in Periodic-Review Systems 281

Problems for Section 5.6 282

Snapshot Application：Tropicana Uses Sophisticated Modeling for Inventory Management 283

5.7 Multiproduct Systems 283

ABCAnalysis 283

Exchange Curves 285

Problems for Section 5.7 288

5.8 Overview of Advanced Topics 289

Multi-echelon Systems 289

Perishable Inventory Problems 290

Snapshot Application：Triad’s Inventory Systems Meet Markets’Needs 291

5.9 Historical Notes and Additional Readings 292

5.10 Summary 293

Additional Problems on Stochastic Inventory Models 294

Appendix 5-A Notational Conventions and Probability Review 300

Appendix 5-B Additional Results and Extensions for the Newsboy Model 301

Appendix 5-C Derivation of the Optimal （Q，R）Policy 304

Appendix 5-D Probability Distributions for Inventory Management 304

Appendix 5-E Glossary of Notation for Chapter 5 308

Bibliography 309

Chapter 6 Supply Chain Management 311

Chapter Overview 311

The Supply Chain as a Strategic Weapon 315

Snapshot Application：Wal-Mart Wins with Solid Supply Chain Management 316

6.1 The Transportation Problem 316

The Greedy Heuristic 319

6.2 Solving Transportation Problems with Linear Programming 320

6.3 Generalizations of the Transportation Problem 322

Infeasible Routes 323

Unbalanced Problems 323

6.4 More General Network Formulations 324

Problems for Sections 6.1-6.4 327

Snapshot Application：IBM Streamlines Its Supply Chain for Spare Parts Using Sophisticated Mathematical Models 328

6.5 Distribution Resource Planning 330

Problems for Section 6.5 332

6.6 Determining Delivery Routes in Supply Chains 332

Practical Issues in Vehicle Scheduling 336

Snapshot Application：Air Products Saves Big with Routing and Scheduling Optimizer 337

Problems for Section 6.6 337

6.7 Designing Products for Supply Chain Efficiency 338

Postponement in Supply Chains 339

Additional Issues in Supply Chain Design 340

Snapshot Application：Dell Computer Designs the Ultimate Supply Chain 342

Problems for Section 6.7 342

6.8 The Role of Information in the Supply Chain 343

The Bullwhip Effect 344

Snapshot Application：Saturn Emerges as an Industry Leader with Scientific Supply Chain Management 347

Electronic Commerce 347

Electronic Data Interchange 348

Web-Based Transactions Systems 349

RFID Technology Provides Faster Product Flow 350

Problems for Section 6.8 351

6.9 Multilevel Distribution Systems 351

Problems for Section 6.9 354

6.10 Designing the Supply Chain in a Global Environment 355

Snapshot Application：Norwegian Company Implements Decision Support System to Streamline Its Supply Chain 356

Snapshot Application：Timken Battles Imports with Bundling 358

Supply Chain Management in a Global Environment 359

Snapshot Application：Digital Equipment Corporation Uses Mathematical Modeling to Plan Its Global Supply Chain 360

Trends in Offshore Outsourcing 360

Problems for Section 6.10 361

6.11 Summary 362

Bibliography 362

Chapter 7 Push and Pull Production Control Systems：MRP and JIT 364

Chapter Overview 364

MRP Basics 367

JIT Basics 369

7.1 The Explosion Calculus 370

Problems for Section 7.1 374

7.2 Alternative Lot-Sizing Schemes 376

EOQ Lot Sizing 376

The Silver-Meal Heuristic 377

Least Unit Cost 378

Part Period Balancing 379

Problems for Section 7.2 380

7.3 Incorporating Lot-Sizing Algorithms into the Explosion Calculus 382

Problems for Section 7.3 383

7.4 Lot Sizing with Capacity Constraints 384

Problems for Section 7.4 387

7.5 Shortcomings of MRP 388

Uncertainty 388

Capacity Planning 389

Rolling Horizons and System Nervousness 390

Additional Considerations 392

Snapshot Application：Raymond Corporation Builds World-Class Manufacturing with MRP Ⅱ 393

Problems for Section 7.5 394

7.6 JIT Fundamentals 395

The Mechanics of Kanban 395

Single Minute Exchange of Dies 397

Advantages and Disadvantages of the Just-in-Time Philosophy 398

Implementation of JIT in the United States 401

Problems for Section 7.6 402

7.7 A Comparison of MRP and JIT 403

7.8 JIT or Lean Production？ 404

7.9 Historical Notes 405

7.10 Summary 406

Additional Problems for Chapter 7 407

Appendix 7-A Optimal Lot Sizing for Time-Varying Demand 411

Appendix 7-B Glossary of Notation for Chapter 7 415

Bibliography 416

Chapter 8 Operations Scheduling 417

Chapter Overview 417

8.1 Production Scheduling and the Hierarchy of Production Decisions 420

8.2 Important Characteristics of Job Shop Scheduling Problems 422

Objectives of Job Shop Management 422

8.3 Job Shop Scheduling Terminology 423

8.4 A Comparison of Specific Sequencing Rules 425

First-Come，First-Served 425

Shortest Processing Time 426

Earliest Due Date 426

Critical Ratio Scheduling 427

8.5 Objectives in Job Shop Management：An Example 428

Problems for Sections 8.1-8.5 429

8.6 An Introduction to Sequencing Theory for a Single Machine 430

Shortest-Processing-Time Scheduling 431

Earliest-Due-Date Scheduling 432

Minimizing the Number of Tardy Jobs 432

Precedence Constraints：Lawler’s Algorithm 433

Snapshot Application：Millions Saved with Scheduling System for Fractional Aircraft Operators 435

Problems for Section 8.6 435

8.7 Sequencing Algorithms for Multiple Machines 437

Scheduling n Jobs on Two Machines 438

Extension to Three Machines 439

The Two-Job Flow Shop Problem 441

Problems for Section 8.7 444

8.8 Stochastic Scheduling：Static Analysis 445

Single Machine 445

Multiple Machines 446

The Two-Machine Flow Shop Case 447

Problems for Section 8.8 448

8.9 Stochastic Scheduling：Dynamic Analysis 449

Selection Disciplines Independent of Job Processing Times 451

Selection Disciplines Dependent onJob Processing Times 452

The cμ Rule 454

Problems for Section 8.9 454

8.10 Assembly Line Balancing 455

Problems for Section 8.10 459

Snapshot Application：Manufacturing Divisions Realize Savings with Scheduling Software 461

8.11 Simulation：A Valuable Scheduling Tool 462

8.12 Post-MRP Production Scheduling Software 463

8.13 Historical Notes 463

8.14 Summary 464

Additional Problems on Scheduling 465

Bibliography 471

Supplement 2 Queuing Theory 473

S2.1 Introduction 473

S2.2 Structural Aspects of Queuing Models 474

S2.3 Notation 475

S2.4 Little’s Formula 476

S2.5 The Exponential and Poisson Distributions in Queuing 476

Aside 477

S2.6 Birth and Death Analysis for the M/M/1 Queue 478

S2.7 Calculation of the Expected System Measures for the M/M/1 Queue 481

S2.8 The Waiting Time Distribution 482

S2.9 Solution of the General Case 484

S2.10 Multiple Servers in Parallel：The M/M/c Queue 485

S2.11 The M/M/1 Queue with a Finite Capacity 489

S2.12 Results for Nonexponential Service Distributions 492

S2.13 The M/G/∞ Queue 493

S2.14 Optimization of Queuing Systems 495

Typical Service System Design Problems 495

Modeling Framework 495

S2.15 Simulation of Queuing Systems 498

Bibliography 499

Chapter 9 Project Scheduling 500

Chapter Overview 500

9.1 Representing a Project as a Network 503

9.2 Critical Path Analysis 505

Finding the Critical Path 508

Problems for Sections 9.1 and 9.2 511

9.3 Time Costing Methods 513

Problems for Section 9.3 517

9.4 Solving Critical Path Problems with Linear Programming 518

Linear Programming Formulation of the Cost-Time Problem 521

Problems for Section 9.4 523

9.5 PERT：Project Evaluation and Review Technique 523

Path Independence 528

Problems for Section 9.5 531

SnapshotApplication：Warner Robins StreamlinesAircraft Maintenance with CCPM Project Management 533

9.6 Resource Considerations 533

Resource Constraints for Single-Project Scheduling 533

Resource Constraints for Multiproject Scheduling 535

Resource Loading Profiles 536

Problems for Section 9.6 538

9.7 Organizational Issues in Project Management 540

9.8 Historical Notes 541

9.9 Project Management Software for the PC 542

Snapshot Application：Project Management Helps United Stay on Schedule 543

Snapshot Application：Thomas Brothers Plans Staffing with Project Management Software 543

Snapshot Application：Florida Power and Light Takes Project Management Seriously 543

9.10 Summary 544

Additional Problems on Project Scheduling 545

Appendix 9-A Glossary of Notation for Chapter 9 548

Bibliography 549

Chapter 10 Facilities Layout and Location 550

Chapter Overview 550

Snapshot Application：Sun Microsystems Pioneers New Flex Office System 553

10.1 The Facilities Layout Problem 554

10.2 Patterns of Flow 555

Activity Relationship Chart 555

From-To Chart 557

10.3 Types of Layouts 559

Fixed Position Layouts 559

Product Layouts 559

Process Layouts 560

Layouts Based on Group Technology 560

Problems for Sections 10.1-10.3 562

10.4 A Prototype Layout Problem and the Assignment Model 564

The Assignment Algorithm 565

Problems for Section 10.4 567

10.5 More Advanced Mathematical Programming Formulations 568

Problem for Section 10.5 569

10.6 Computerized Layout Techniques 569

CRAFT 570

COFAD 574

ALDEP 575

CORELAP 576

PLANET 577

Computerized Methods versus Human Planners 577

Dynamic Plant Layouts 578

Other Computer Methods 578

Problems for Section 10.6 579

10.7 Flexible Manufacturing Systems 582

Advantages of Flexible Manufacturing Systems 584

Disadvantages of Flexible Manufacturing Systems 584

Decision Making and Modeling of the FMS 585

The Future of FMS 588

Problems for Section 10.7 590

10.8 Locating New Facilities 590

Snapshot Application：Kraft Foods Uses Optimization and Simulation to Determine Best Layout 591

Measures of Distance 592

Problems for Section 10.8 593

10.9 The Single-Facility Rectilinear Distance Location Problem 593

Contour Lines 596

Minimax Problems 597

Problems for Section 10.9 600

10.10 Euclidean Distance Problems 601

The Gravity Problem 601

The Straight-Line Distance Problem 602

Problems for Section 10.10 603

10.11 Other Location Models 604

Locating Multiple Facilities 605

Further Extensions 606

Problems for Section 10.11 608

10.12 Historical Notes 609

10.13 Summary 610

Additional Problems on Layout and Location 611

Spreadsheet Problems for Chapter 10 616

Appendix 10-A Finding Centroids 617

Appendix 10-B Computing Contour Lines 619

Bibliography 622

Chapter 11 Quality and Assurance 624

Chapter Overview 624

Overview of This Chapter 628

11.1 Statistical Basis of Control Charts 629

Problems for Section 11.1 631

11.2 Control Charts for Variables：The-X and R Charts 633

-X Charts 636

Relationship to Classical Statistics 636

R Charts 638

Problems for Section 11.2 639

11.3 Control Charts for Attributes：The p Chart 641

p Charts for Varying Subgroup Sizes 643

Problems for Section 11.3 644

11.4 The c Chart 646

Problems for Section 11.4 648

11.5 Classical Statistical Methods and Control Charts 649

Problem for Section 11.5 649

11.6 Economic Design ofXCharts 650

Problems for Section 11.6 656

11.7 Overview of Acceptance Sampling 657

Snapshot Application：Navistar Scores with Six-Sigma Quality Program 659

11.8 Notation 660

11.9 Single Sampling for Attributes 660

Derivation of the OC Curve 662

Problems for Section 11.9 664

11.10 Double Sampling Plans for Attributes 665

Problems for Section11. 10 666

11.11 Sequential Sampling Plans 667

Problems for Section 11.11 671

11.12 Average Outgoing Quality 672

Snapshot Application：Motorola Leads the Way with Six-Sigma Quality Programs 674

Problems for Section 11.12 674

11.13 Total Quality Management 675

Definitions 675

Listening to the Customer 675

Competition Based on Quality 677

Organizing for Quality 678

Benchmarking Quality 679

The Deming Prize and the Baldrige Award 680

ISO 9000 682

Quality：The Bottom Line 683

11.14 Designing Quality into the Product 684

Design，Manufacturing，and Quality 686

11.15 Historical Notes 688

11.16 Summary 689

Additional Problems on Quality and Assurance 691

Appendix 11-A Approximating Distributions 695

Appendix 11-B Glossary of Notation for Chapter 11 on Quality and Assurance 697

Bibliography 698

Chapter 12 Reliability and Maintainability 700

Chapter Overview 700

12.1 Reliability of a Single Component 704

Introduction to Reliability Concepts 704

Preliminary Notation and Definitions 705

The Exponential Failure Law 707

Problems for Section 12.1 710

12.2 Increasing and Decreasing Failure Rates 712

Problems for Section 12.2 714

12.3 The Poisson Process in Reliability Modeling 715

Series Systems Subject to Purely Random Failures 718

Problems for Section 12.3 719

12.4 Failures of Complex Equipment 720

Components in Series 720

Components in Parallel 721

Expected Value Calculations 721

K Out of N Systems 722

Problems for Section 12.4 724

12.5 Introduction to Maintenance Models 724

12.6 Deterministic Age Replacement Strategies 726

The Optimal Policy in the Basic Case 726

A General Age Replacement Model 728

Problems for Section 12.6 732

12.7 Planned Replacement under Uncertainty 732

Planned Replacement for a Single Item 732

Block Replacement for a Group of Items 736

Problems for Section 12.7 738

12.8 Analysis of Warranty Policies 740

The Free Replacement Warranty 740

The Pro Rata Warranty 742

Extensions and Criticisms 744

Problems for Section 12.8 744

12.9 Software Reliability 745

Snapshot Application：Reliability-Centered Maintenance Improves Operations at Three Mile Island Nuclear Plant 746

12.10 Historical Notes 747

12.11 Summary 748

Additional Problems on Reliability and Maintainability 749

Appendix 12-A Glossary of Notation on Reliability and Maintainability 751

Bibliography 753

Appendix：Tables 754

Index 772