生产与运作分析 第六版 英文PDF|Epub|txt|kindle电子书版本网盘下载

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Chapter 1 Strategy and Competition1

Chapter Overview1

Snapshot Application：Apple Adopts a New Business Strategy and Shifts Its Core Competency from Computers to Portable Music3

1.1 Manufacturing Matters5

Manufacturing Jobs Outlook6

1.2 A Framework for Operations Strategy7

Strategic Dimensions8

1.3 The Classical View of Operations Strategy9

Time Horizon9

Focus11

Evaluation12

Consistency12

1.4 Competing in the Global Marketplace14

Problems for Sections 1.1-1.416

Snapshot Application：Global Manufacturing Strategies in the Automobile Industry17

1.5 Strategic Initiatives：Reengineering the Business Process18

1.6 Strategic Initiatives：Just-in-Time21

1.7 Strategic Initiatives：Time-Based Competition23

1.8 Strategic Initiatives：Competing on Quality24

Problems for Sections 1.5-1.826

1.9 Matching Process and Product Life Cycles27

The Product Life Cycle27

The Process Life Cycle28

The Product-Process Matrix29

Problems for Section 1.931

1.10 Learning and Experience Curves31

Learning Curves32

Experience Curves34

Learning and Experience Curves and Manufacturing Strategy36

Problems for Section 1.1036

1.11 Capacity Growth Planning：A Long-Term Strategic Problem38

Economies of Scale and Economies of Scope38

Make or Buy：A Prototype Capacity Expansion Problem39

Dynamic Capacity Expansion Policy40

Issues in Plant Location44

Problems for Section 1.1146

1.12 Summary47

Additional Problems for Chapter 148

Appendix 1-A Present Worth Calculations50

Bibliography51

Chapter 2 Forecasting52

Chapter Overview52

2.1 The Time Horizon in Forecasting55

2.2 Characteristics of Forecasts56

2.3 Subjective Forecasting Methods56

2.4 Objective Forecasting Methods57

Causal Models57

Time Series Methods58

Snapshot Application：Advanced Forecasting，Inc.，Serves the Semiconductor Industry59

Problems for Sections 2.1-2.459

2.5 Notation Conventions61

2.6 Evaluating Forecasts61

Problems for Section 2.663

2.7 Methods for Forecasting Stationary Series64

MovingAverages64

Problems on Moving Averages67

Exponential Smoothing67

Multiple-Step-Ahead Forecasts71

Comparison of Exponential Smoothing and MovingAverages72

Problems for Section 2.773

Snapshot Application：Sport Obermeyer Slashes Costs with Improved Forecasting74

2.8 Trend-Based Methods75

Regression Analysis75

Problems for Section 2.876

Double Exponential Smoothing Using Holt’s Method77

More Problems for Section 2.878

2.9 Methods for Seasonal Series79

Seasonal Factors for Stationary Series79

Seasonal Decomposition Using Moving Averages81

Problems for Section 2.983

Winters ’s Method for Seasonal Problems84

More Problems for Section 2.989

2.10 Box-Jenkins Models89

Estimating the Autocorrelation Function90

The Autoregressive Process93

The Moving-Average Process94

Mixtures：ARMA Models96

ARIMA Models96

Using ARIMA Models for Forecasting98

Summary of the Steps Required for Building ARIMA Models99

Case Study：Using Box-Jenkins Methodology to Predict Monthly International Airline Passenger Totals100

SnapshotApplication：A Simple ARIMA Model Predicts the Performance of the U.S.Economy104

Box-Jenkins Modeling—A Critique104

Problems for Section 2.10104

2.11 Practical Considerations105

Model Identification and Monitoring105

Simple versus Complex Time Series Methods106

2.12 Overview of Advanced Topics in Forecasting107

Simulation as a Forecasting Tool107

Forecasting Demand in the Presence of Lost Sales108

2.13 Linking Forecasting and Inventory Management110

Snapshot Application：Predicting Economic Recessions111

2.14 Historical Notes and Additional Topics112

2.15 Summary113

Additional Problems on Forecasting113

Appendix 2-A Forecast Errors for Moving Averages and Exponential Smoothing118

Appendix 2-B Derivation of the Equations for the Slope and Intercept for Regression Analysis120

Appendix 2-C Glossary of Notation for Chapter 2122

Bibliography122

Chapter 3 Aggregate Planning124

Chapter Overview124

3.1 Aggregate Units of Production127

3.2 Overview of the Aggregate Planning Problem128

3.3 Costs in Aggregate Planning130

Problems for Sections 3.1-3.3132

3.4 A Prototype Problem133

Evaluation of a Chase Strategy（Zero Inventory Plan）135

Evaluation of the Constant Workforce Plan136

Mixed Strategies and Additional Constraints138

Problems for Section 3.4139

3.5 Solution of Aggregate Planning Problems by Linear Programming141

Cost Parameters and Given Information141

Problem Variables142

Problem Constraints142

Rounding the Variables143

Extensions144

Other Solution Methods146

3.6 Solving Aggregate Planning Problems by Linear Programming：An Example147

Problems for Sections 3.5 and 3.6149

3.7 The Linear Decision Rule152

3.8 Modeling Management Behavior153

Problems for Sections 3.7 and 3.8155

3.9 Disaggregating Aggregate Plans155

Snapshot Application：Welch’s Uses Aggregate Planning for Production Scheduling157

Problems for Section 3.9158

3.10 Production Planning on a Global Scale158

3.11 Practical Considerations159

3.12 Historical Notes160

3.13 Summary161

Additional Problems on Aggregate Planning162

Appendix 3-A Glossary of Notation for Chapter 3167

Bibliography168

Supplement 1 Linear Programming169

S1.1 Introduction169

S1.2 A Protorype Linear Programming Problem169

S1.3 Statement of the General Problem171

Definitions of Commonly Used Terms172

Features of Linear Programs173

S1.4 Solving Linear Programming Problems Graphically174

Graphing Linear Inequalities174

Graphing the Feasible Region176

Finding the Optimal Solution177

Identifying the Optimal Solution Directly by Graphical Means179

S1.5 The Simplex Method：An Overview180

S1.6 Solving Linear Programming Problems with Excel181

Entering Large Problems Efficiently185

S1.7 Interpreting the Sensitivity Report187

Shadow Prices187

Objective Function Coefficients and Right-Hand Sides188

Adding a New Variable188

Using Sensitivity Analysis189

S1.8 Recognizing Special Problems191

Unbounded Solutions191

Empty Feasible Region192

Degeneracy194

Multiple Optimal Solutions194

Redundant Constraints194

S1.9 The Application of Linear Programming to Production and Operations Analysis195

Bibliography197

Chapter 4 Inventory Control Subject to Known Demand198

Chapter Overview198

4.1 Types of Inventories201

4.2 Motivation for Holding Inventories202

4.3 Characteristics of Inventory Systems203

4.4 Relevant Costs204

Holding Cost204

Order Cost206

Penalty Cost207

Problems for Sections 4.1-4.4208

4.5 The EOQ Model210

The Basic Model210

Inclusion of Order Lead Time213

Sensitivity214

EOQ and JIT215

Problems for Section 4.5216

4.6 Extension to a Finite Production Rate218

Problems for Section 4.6219

4.7 Quantity Discount Models220

OptimalPolicy for All-Units Discount Schedule221

Summary of the Solution Technique for All-Units Discounts223

Incremental Quantity Discounts223

Summary of the Solution Technique for Incremental Discounts225

Other Discount Schedules225

Problems for Section 4.7226

4.8 Resource-Constrained Multiple Product Systems227

Problems for Section 4.8230

4.9 EOQ Models for Production Planning230

Problems for Section 4.9234

4.10 Power-of-Two Policies235

4.11 Historical Notes and Additional Topics237

Snapshot Application：Mervyn’s Recognized for State-of-the-Art Inventory Control System238

4.12 Summary239

Additional Problems on Deterministic Inventory Models240

Appendix 4-A Mathematical Derivations for Multiproduct Constrained EOQ Systems244

Appendix 4-B Glossary of Notation for Chapter 4246

Bibliography246

Chapter 5 Inventory Control Subject to Uncertain Demand248

Chapter Overview248

Overview of Models Treated in This Chapter252

5.1 The Nature of Randomness253

5.2 Optimization Criterion255

Problems for Sections 5.1 and 5.2256

5.3 The Newsboy Model257

Notation257

Development of the Cost Function258

Determining the Optimal Policy259

Optimal Policy for Discrete Demand261

Extension to Include Starting Inventory261

Snapshot Application：Using Inventory Models to Manage the Seed-Corn Supply Chain at Syngenta262

Extension to Multiple Planning Periods263

Problems for Section 5.3264

5.4 Lot Size-Reorder Point Systems266

Describing Demand267

Decision Variables267

Derivation of the Expected Cost Function267

The Cost Function269

Inventory Level versus Inventory Position271

5.5 Service Levels in（Q，R）Systems272

Type 1 Service272

Type 2 Service273

Optimal（Q，R）Policies Subject to Type 2 Constraint274

Imputed Shortage Cost275

Scaling of Lead Time Demand276

Estimating Sigma When Inventory Control and Forecasting Are Linked276

Lead Time Variability277

Calculations in Excel278

Negative Safety Stock278

Problems for Sections 5.4 and 5.5279

5.6 Additional Discussion of Periodic-Review Systems281

（s，S）Policies281

Service Levels in Periodic-Review Systems281

Problems for Section 5.6282

Snapshot Application：Tropicana Uses Sophisticated Modeling for Inventory Management283

5.7 Multiproduct Systems283

ABCAnalysis283

Exchange Curves285

Problems for Section 5.7288

5.8 Overview of Advanced Topics289

Multi-echelon Systems289

Perishable Inventory Problems290

Snapshot Application：Triad’s Inventory Systems Meet Markets’Needs291

5.9 Historical Notes and Additional Readings292

5.10 Summary293

Additional Problems on Stochastic Inventory Models294

Appendix 5-A Notational Conventions and Probability Review300

Appendix 5-B Additional Results and Extensions for the Newsboy Model301

Appendix 5-C Derivation of the Optimal （Q，R）Policy304

Appendix 5-D Probability Distributions for Inventory Management304

Appendix 5-E Glossary of Notation for Chapter 5308

Bibliography309

Chapter 6 Supply Chain Management311

Chapter Overview311

The Supply Chain as a Strategic Weapon315

Snapshot Application：Wal-Mart Wins with Solid Supply Chain Management316

6.1 The Transportation Problem316

The Greedy Heuristic319

6.2 Solving Transportation Problems with Linear Programming320

6.3 Generalizations of the Transportation Problem322

Infeasible Routes323

Unbalanced Problems323

6.4 More General Network Formulations324

Problems for Sections 6.1-6.4327

Snapshot Application：IBM Streamlines Its Supply Chain for Spare Parts Using Sophisticated Mathematical Models328

6.5 Distribution Resource Planning330

Problems for Section 6.5332

6.6 Determining Delivery Routes in Supply Chains332

Practical Issues in Vehicle Scheduling336

Snapshot Application：Air Products Saves Big with Routing and Scheduling Optimizer337

Problems for Section 6.6337

6.7 Designing Products for Supply Chain Efficiency338

Postponement in Supply Chains339

Additional Issues in Supply Chain Design340

Snapshot Application：Dell Computer Designs the Ultimate Supply Chain342

Problems for Section 6.7342

6.8 The Role of Information in the Supply Chain343

The Bullwhip Effect344

Snapshot Application：Saturn Emerges as an Industry Leader with Scientific Supply Chain Management347

Electronic Commerce347

Electronic Data Interchange348

Web-Based Transactions Systems349

RFID Technology Provides Faster Product Flow350

Problems for Section 6.8351

6.9 Multilevel Distribution Systems351

Problems for Section 6.9354

6.10 Designing the Supply Chain in a Global Environment355

Snapshot Application：Norwegian Company Implements Decision Support System to Streamline Its Supply Chain356

Snapshot Application：Timken Battles Imports with Bundling358

Supply Chain Management in a Global Environment359

Snapshot Application：Digital Equipment Corporation Uses Mathematical Modeling to Plan Its Global Supply Chain360

Trends in Offshore Outsourcing360

Problems for Section 6.10361

6.11 Summary362

Bibliography362

Chapter 7 Push and Pull Production Control Systems：MRP and JIT364

Chapter Overview364

MRP Basics367

JIT Basics369

7.1 The Explosion Calculus370

Problems for Section 7.1374

7.2 Alternative Lot-Sizing Schemes376

EOQ Lot Sizing376

The Silver-Meal Heuristic377

Least Unit Cost378

Part Period Balancing379

Problems for Section 7.2380

7.3 Incorporating Lot-Sizing Algorithms into the Explosion Calculus382

Problems for Section 7.3383

7.4 Lot Sizing with Capacity Constraints384

Problems for Section 7.4387

7.5 Shortcomings of MRP388

Uncertainty388

Capacity Planning389

Rolling Horizons and System Nervousness390

Additional Considerations392

Snapshot Application：Raymond Corporation Builds World-Class Manufacturing with MRP Ⅱ393

Problems for Section 7.5394

7.6 JIT Fundamentals395

The Mechanics of Kanban395

Single Minute Exchange of Dies397

Advantages and Disadvantages of the Just-in-Time Philosophy398

Implementation of JIT in the United States401

Problems for Section 7.6402

7.7 A Comparison of MRP and JIT403

7.8 JIT or Lean Production？404

7.9 Historical Notes405

7.10 Summary406

Additional Problems for Chapter 7407

Appendix 7-A Optimal Lot Sizing for Time-Varying Demand411

Appendix 7-B Glossary of Notation for Chapter 7415

Bibliography416

Chapter 8 Operations Scheduling417

Chapter Overview417

8.1 Production Scheduling and the Hierarchy of Production Decisions420

8.2 Important Characteristics of Job Shop Scheduling Problems422

Objectives of Job Shop Management422

8.3 Job Shop Scheduling Terminology423

8.4 A Comparison of Specific Sequencing Rules425

First-Come，First-Served425

Shortest Processing Time426

Earliest Due Date426

Critical Ratio Scheduling427

8.5 Objectives in Job Shop Management：An Example428

Problems for Sections 8.1-8.5429

8.6 An Introduction to Sequencing Theory for a Single Machine430

Shortest-Processing-Time Scheduling431

Earliest-Due-Date Scheduling432

Minimizing the Number of Tardy Jobs432

Precedence Constraints：Lawler’s Algorithm433

Snapshot Application：Millions Saved with Scheduling System for Fractional Aircraft Operators435

Problems for Section 8.6435

8.7 Sequencing Algorithms for Multiple Machines437

Scheduling n Jobs on Two Machines438

Extension to Three Machines439

The Two-Job Flow Shop Problem441

Problems for Section 8.7444

8.8 Stochastic Scheduling：Static Analysis445

Single Machine445

Multiple Machines446

The Two-Machine Flow Shop Case447

Problems for Section 8.8448

8.9 Stochastic Scheduling：Dynamic Analysis449

Selection Disciplines Independent of Job Processing Times451

Selection Disciplines Dependent onJob Processing Times452

The cμ Rule454

Problems for Section 8.9454

8.10 Assembly Line Balancing455

Problems for Section 8.10459

Snapshot Application：Manufacturing Divisions Realize Savings with Scheduling Software461

8.11 Simulation：A Valuable Scheduling Tool462

8.12 Post-MRP Production Scheduling Software463

8.13 Historical Notes463

8.14 Summary464

Additional Problems on Scheduling465

Bibliography471

Supplement 2 Queuing Theory473

S2.1 Introduction473

S2.2 Structural Aspects of Queuing Models474

S2.3 Notation475

S2.4 Little’s Formula476

S2.5 The Exponential and Poisson Distributions in Queuing476

Aside477

S2.6 Birth and Death Analysis for the M/M/1 Queue478

S2.7 Calculation of the Expected System Measures for the M/M/1 Queue481

S2.8 The Waiting Time Distribution482

S2.9 Solution of the General Case484

S2.10 Multiple Servers in Parallel：The M/M/c Queue485

S2.11 The M/M/1 Queue with a Finite Capacity489

S2.12 Results for Nonexponential Service Distributions492

S2.13 The M/G/∞ Queue493

S2.14 Optimization of Queuing Systems495

Typical Service System Design Problems495

Modeling Framework495

S2.15 Simulation of Queuing Systems498

Bibliography499

Chapter 9 Project Scheduling500

Chapter Overview500

9.1 Representing a Project as a Network503

9.2 Critical Path Analysis505

Finding the Critical Path508

Problems for Sections 9.1 and 9.2511

9.3 Time Costing Methods513

Problems for Section 9.3517

9.4 Solving Critical Path Problems with Linear Programming518

Linear Programming Formulation of the Cost-Time Problem521

Problems for Section 9.4523

9.5 PERT：Project Evaluation and Review Technique523

Path Independence528

Problems for Section 9.5531

SnapshotApplication：Warner Robins StreamlinesAircraft Maintenance with CCPM Project Management533

9.6 Resource Considerations533

Resource Constraints for Single-Project Scheduling533

Resource Constraints for Multiproject Scheduling535

Resource Loading Profiles536

Problems for Section 9.6538

9.7 Organizational Issues in Project Management540

9.8 Historical Notes541

9.9 Project Management Software for the PC542

Snapshot Application：Project Management Helps United Stay on Schedule543

Snapshot Application：Thomas Brothers Plans Staffing with Project Management Software543

Snapshot Application：Florida Power and Light Takes Project Management Seriously543

9.10 Summary544

Additional Problems on Project Scheduling545

Appendix 9-A Glossary of Notation for Chapter 9548

Bibliography549

Chapter 10 Facilities Layout and Location550

Chapter Overview550

Snapshot Application：Sun Microsystems Pioneers New Flex Office System553

10.1 The Facilities Layout Problem554

10.2 Patterns of Flow555

Activity Relationship Chart555

From-To Chart557

10.3 Types of Layouts559

Fixed Position Layouts559

Product Layouts559

Process Layouts560

Layouts Based on Group Technology560

Problems for Sections 10.1-10.3562

10.4 A Prototype Layout Problem and the Assignment Model564

The Assignment Algorithm565

Problems for Section 10.4567

10.5 More Advanced Mathematical Programming Formulations568

Problem for Section 10.5569

10.6 Computerized Layout Techniques569

CRAFT570

COFAD574

ALDEP575

CORELAP576

PLANET577

Computerized Methods versus Human Planners577

Dynamic Plant Layouts578

Other Computer Methods578

Problems for Section 10.6579

10.7 Flexible Manufacturing Systems582

Advantages of Flexible Manufacturing Systems584

Disadvantages of Flexible Manufacturing Systems584

Decision Making and Modeling of the FMS585

The Future of FMS588

Problems for Section 10.7590

10.8 Locating New Facilities590

Snapshot Application：Kraft Foods Uses Optimization and Simulation to Determine Best Layout591

Measures of Distance592

Problems for Section 10.8593

10.9 The Single-Facility Rectilinear Distance Location Problem593

Contour Lines596

Minimax Problems597

Problems for Section 10.9600

10.10 Euclidean Distance Problems601

The Gravity Problem601

The Straight-Line Distance Problem602

Problems for Section 10.10603

10.11 Other Location Models604

Locating Multiple Facilities605

Further Extensions606

Problems for Section 10.11608

10.12 Historical Notes609

10.13 Summary610

Additional Problems on Layout and Location611

Spreadsheet Problems for Chapter 10616

Appendix 10-A Finding Centroids617

Appendix 10-B Computing Contour Lines619

Bibliography622

Chapter 11 Quality and Assurance624

Chapter Overview624

Overview of This Chapter628

11.1 Statistical Basis of Control Charts629

Problems for Section 11.1631

11.2 Control Charts for Variables：The-X and R Charts633

-X Charts636

Relationship to Classical Statistics636

R Charts638

Problems for Section 11.2639

11.3 Control Charts for Attributes：The p Chart641

p Charts for Varying Subgroup Sizes643

Problems for Section 11.3644

11.4 The c Chart646

Problems for Section 11.4648

11.5 Classical Statistical Methods and Control Charts649

Problem for Section 11.5649

11.6 Economic Design ofXCharts650

Problems for Section 11.6656

11.7 Overview of Acceptance Sampling657

Snapshot Application：Navistar Scores with Six-Sigma Quality Program659

11.8 Notation660

11.9 Single Sampling for Attributes660

Derivation of the OC Curve662

Problems for Section 11.9664

11.10 Double Sampling Plans for Attributes665

Problems for Section11. 10666

11.11 Sequential Sampling Plans667

Problems for Section 11.11671

11.12 Average Outgoing Quality672

Snapshot Application：Motorola Leads the Way with Six-Sigma Quality Programs674

Problems for Section 11.12674

11.13 Total Quality Management675

Definitions675

Listening to the Customer675

Competition Based on Quality677

Organizing for Quality678

Benchmarking Quality679

The Deming Prize and the Baldrige Award680

ISO 9000682

Quality：The Bottom Line683

11.14 Designing Quality into the Product684

Design，Manufacturing，and Quality686

11.15 Historical Notes688

11.16 Summary689

Additional Problems on Quality and Assurance691

Appendix 11-A Approximating Distributions695

Appendix 11-B Glossary of Notation for Chapter 11 on Quality and Assurance697

Bibliography698

Chapter 12 Reliability and Maintainability700

Chapter Overview700

12.1 Reliability of a Single Component704

Introduction to Reliability Concepts704

Preliminary Notation and Definitions705

The Exponential Failure Law707

Problems for Section 12.1710

12.2 Increasing and Decreasing Failure Rates712

Problems for Section 12.2714

12.3 The Poisson Process in Reliability Modeling715

Series Systems Subject to Purely Random Failures718

Problems for Section 12.3719

12.4 Failures of Complex Equipment720

Components in Series720

Components in Parallel721

Expected Value Calculations721

K Out of N Systems722

Problems for Section 12.4724

12.5 Introduction to Maintenance Models724

12.6 Deterministic Age Replacement Strategies726

The Optimal Policy in the Basic Case726

A General Age Replacement Model728

Problems for Section 12.6732

12.7 Planned Replacement under Uncertainty732

Planned Replacement for a Single Item732

Block Replacement for a Group of Items736

Problems for Section 12.7738

12.8 Analysis of Warranty Policies740

The Free Replacement Warranty740

The Pro Rata Warranty742

Extensions and Criticisms744

Problems for Section 12.8744

12.9 Software Reliability745

Snapshot Application：Reliability-Centered Maintenance Improves Operations at Three Mile Island Nuclear Plant746

12.10 Historical Notes747

12.11 Summary748

Additional Problems on Reliability and Maintainability749

Appendix 12-A Glossary of Notation on Reliability and Maintainability751

Bibliography753

Appendix：Tables754

Index772