Production and
Operations Analysis
Sixth Edition
Steven Nahmias
Santa Clara University
Me
Graw
Hill
Boston Burr Ridge, IL Dubuque, IA Madison, Wl New York San Francisco St. Louis
Bangkok Bogota Caracas Kuala Lumpur Lisbon London Madrid Mexico City
Milan Montreal New Delhi Santiago Seoul Singapore Sydney Taipei Toronto
Contents
About the Author xv
Preface xvi
Introduction xvii
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
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 S
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 2 7
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
1.12 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
2.6
2.7
2.8
Notation Conventions 61
Evaluating Forecasts 61
Problems for Section 2.6 63
Methods for Forecasting Stationary Series 64
Moving Averages 64
Problems on Moving Averages 67
Exponential Smoothing 67
Multiple-Step-Ahead Forecasts 71
Comparison of Exponential Smoothing and
Moving A verages 72
Problems for Section 2.7 73
Snapshot Application: Sport Obermeyer Slashes
Costs with Improved Forecasting 74
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
viii Contents
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 Methodfor 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 Requiredfor Building
ARIMA Models 99
Case Study: Using Box-Jenkins Methodology to
Predict Monthly International Airline
Passenger Totals 100
Snapshot Application: 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.d.3 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
Plann ingfor 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
51.1 Introduction 169
51.2 A Prototype Linear Programming
Problem 169
51.3 Statement of the General Problem 171
Definitions of Commonly Used Terms 172
Features of Linear Programs 173
Contents
ST.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
51.5 The Simplex Method: An Overview 180
51.6 Solving Linear Programming Problems
with Excel 181
Entering Large Problems Efficiently 185
51.7 Interpreting the Sensitivity Report 187
Shadow Prices 187
Objective Function Coefficients and RightHand Sides 188
Adding a New Variable 188
Using Sensitivity Analysis 189
51.8 Recognizing Special Problems 191
Unbounded Solutions 191
Empty Feasible Region 192
Degeneracy 194
Multiple Optimal Solutions 194
Redundant Constraints 194
51.9 The Application of Linear Programming
to Production and Operations Analysis 195
Bibliography 197
Summary, of the Solution Technique for
AlUUnits 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
ProblemsfanSection 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
Append ix 4-B Glossary of Notation for
Chapter 4 246
Bibliography 246
Chapter 5
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
Optimal Policyfor All-Units Discount Schedule 221
ix
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
x Contents
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 2 76
Estimating Sigma When Inventory Control
and Forecasting A re Linked 2 76
*Lead Time Variability 277
Calculations in Excel 278
Negative Safety Stock 2 78
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 344Snapshot 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
Contents
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
7.1
7.2
7.3
7.4
7.5
7.6
Implementation of JIT in the United States 401
Problems for Section 7.6 402
7.7
7.8
7.9
A Comparison of MRP and JIT 403
JIT or Lean Production? 404
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
364
MRP Basics 367
JIT Basics 369
The Explosion Calculus 370
Problems for Section 7.1 374
Alternative Lot-Sizing Schemes 376
EOQ Lot Sizing 376
The Silver-Meal Heuristic 377
Least Unit Cost 378
Part Period Balancing 3 79
Problems for Section 7.2 380
Incorporating Lot-Sizing Algorithms into the
Explosion Calculus . 382
Problems for Section 7.3 383
Lot Sizing with Capacity Constraints 384
Problems for Section 7.4 387
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
JIT Fundamentals 395
The Mechanics ofKanban 395
Single Minute Exchange of Dies 397
Advantages and Disadvantages of the Justin-Time Philosophy 398
xi
8.3
8.4
8.5
8.6
8.7
8.8
Objectives of Job Shop Management 422
Job Shop Scheduling Terminology 423
A Comparison of Specific Sequencing
Rules 425
First-Come, First-Served 425
Shortest Processing Time 426
Earliest Due Date 426
Critical Ratio Scheduling 427
Objectives in Job Shop Management:
An Example 428
Problems for Sections 8.1-8.5 429
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
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
Stochastic Scheduling: Static Analysis 445
Single Machine 445
Multiple Machines 446
xii
Contents
The Two-Machine Flow Shop Case- 447
Problems for Section 8.8 448
8.9
Stochastic Scheduling: Dynamic Analysis 449
Chapter Overview 500
9.1 Representing a Project as a
Network 503
9.2 Critical Path Analysis 505
Selection Disciplines Independent of
Job Processing Times 451
Selection Disciplines Dependent on
Job Processing Times. 452
The cfi Rule 454
Problems for Section 8.9 454
8.10
Assembly Line Balancing
Finding the Critical Path 508
Problems for Sections 9.1 and 9.2 511
9.3
455
Problems for Section 8.10 459
Snapshot Application: Manufacturing
Divisions Realize Savings with Scheduling
Software 461
8.11
8.12
8.13
8.14
Simulation: A Valuable Scheduling Tool
Post-MRP Production Scheduling
Software 463
Historical Notes 463
Summary 464
Chapter 9
Project Scheduling 500
9.4
462
9:5". PERT: Project Evaluation and Review
Technique 523
Path Independence 528
Problems for Section 9.5 531
Snapshot Application: Warner Robins Streamlines
Aircraft Maintenance with CCPM Project
Management 533
471
52.3
52.4
52.5
Introduction 473
Structural Aspects of Queuing
Models 474
Notation 475
Little's Formula 476
The Exponential and Poisson Distributions
in Queuing 476
Aside
52.6
52.7
52.8
52.9
52.10
52.11
52.12
52.13
52.14
477'
Birth and Death Analysis for the M/M/l
Queue 478
Calculation of the Expected System Measures
for the M/M/l Queue 481
The Waiting Time Distribution 482
Solution of the General Case 484
Multiple Servers in Parallel: The M/M/c
Queue 485
The M/M/l Queue with a Finite
Capacity 489
Results for Nonexponential Service
Distributions 492
The M/G/oo. Queue 493
Optimization of Queuing Systems 495
Typical Service System Design
Problems 495
Modeling Framework 495
52.15 Simulation of Queuing Systems 498
Bibliography 499
Solving Critical Path Problems with Linear
Programming 518
Linear Programming Formulation of the
Cost-Time Problem 521
Problems for Section 9.4 523
Supplement 2 Queuing Theory 473
52.1
52.2
513
Problems for Section 9.3 517
Additional Problems on Scheduling 465
Bibliography
Time Gosting Methods
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
9.8
9.9
Organizational Issues in Project
Management 540
Historical Notes 541
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
Contents xiii
Chapter 10
Facilities Layout and Location
10.9
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
ProblemfarSection 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 5 78
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
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 Thee 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 of X Charts 650
Problems for Section 11.6 656
xiv
Contents
11.7
Overview of Acceptance Sampling 657
12.2
Increasing and Decreasing Failure
Rates 712
12.3
The Poisson Process in Reliability
Modeling 715
Snapshot Application: Navistar Scores with
Six-Sigma Quality Program 659
11.8
11.9
Notation 660
Single Sampling for Attributes 660
Problems for Section 12.2 714
Derivation of the OC Curve 662
Problems for Section 11.9 664
Series Systems Subject to Purely Random
Failures 718
Problems for Section 12.3 719
* 11.10 Double Sampling Plans for Attributes 665
Problems for Section 11.10 666
11.11
11.12
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
12.4
Sequential Sampling Plans 667
ProblemsfarSectionll.il 671
Average Outgoing Quality 672
Designing Quality into the Product 684
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
12.6
12.7
Appendix 11-A Approximating
Distributions 695
Append ix 11 -B Glossary of Notation for
Chapter 11 on Quality and Assurance 697
Bibliography 698
*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
Chapter 12
Reliability and Maintainability
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
Historical Notes 688
Summary 689
Additional Problems on Quality and
Assurance 691
Introduction to Maintenance Models 724
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
Design, Manufacturing, and Quality 686
11.15
11.16
Failures of Complex Equipment 720
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
Appendix 12-A Glossary of Notation on
Reliability and Maintainability 751
Bibliography 753
Appendix: Tables 754
Index 772