Contents
PREFACE = xxi
PART 1 DETERMINISTIC MODELS
CHAPTER 1 INTRODUCTION TO MANAGEMENT SCIENCE = 1
1.1 What is Management Science? = 2
1.2 The History of management Science = 3
1.3 The Methodology of Management Science = 3
1.3.1 Defining the Problem = 4
1.3.2 Developing a Mathematical Model and Collecting Data = 4
1.3.3 Soving the Mathematical Model = 6
1.3.4 Validating, Implementing, and Monitoring the Solution = 7
1.3.5 Modifying the Model = 7
1.4 Uses and Advantages of Management Science Models = 8
Summary = 9
Video Case : Launching into Managerial Science = 10
CHAPTER 2 THE ART AND SCIENCE OF BUILDING DETERMINISTIC MODELS = 11
2.1 General Steps and Techniques of Building Mathematical Models = 12
2.1.1 Identifying the Decision Variables = 12
2.1.2 Identifying the Problem Data = 13
2.1.3 Identifying the Objective Function = 14
2.1.4 Identifying the Constraints = 15
2.2 Additional Examples of Problem Formulation = 18
2.2.1 Examples of Network Problems : The Transportation Problem = 18
2.2.2 Examples of Network Problems : The Maximum-Flow Problem of Hexxon Oil Company = 25
2.2.3 Portfolio Management : The Use of 0-1 Integer Variables = 29
2.2.4 A Location Problem = 33
2.2.5 The Container-Design Problem = 37
2.3 Classification of Mathematical Models = 41
2.3.1 Classifications Based on Problem Data = 41
2.3.2 Classifications Based on the Constraints = 41
2.3.3 Classifications Based on the Objective Function = 43
2.3.4 Classifications Based on the Variables = 44
Additional Managerial Considerations = 45
Resolving Ambiguities in the Problem Definition = 45
Alternative Problem Formulations = 45
Data Collections Issues = 46
Implementation Issues = 47
Summary = 47
Exercises = 48
Video Case : Squeezing Profits = 57
CHAPTER 3 APPLICATIONS OF LINEAR PROGRAMMING = 58
3.1 Linear Programming Models for Product-Mix Decisions = 58
3.1.1 Identifying the Decision Variables = 60
3.1.2 Identifying the Objective Function = 60
3.1.3 Identifying the Constraints = 60
3.1.4 Complete Formulation and Solution of the Product-Mix Problem of BlubberMaid, Inc. = 61
3.2 Linear Programming Models for Make-or-Buy Decisions = 62
3.2.1 Indentifying the Decision Variables = 63
3.2.2 Indentifying the Objective Function = 63
3.2.3 Identifying the Constraints = 64
3.2.4 Complete Formulation and Solution of the Make-or-Buy Problem of MTV Steel Company = 65
3.3 Linear Programming Models for Diet Problems = 66
3.3.1 Identifying the Decision Variables = 67
3.3.2 Identifying the Ovjective Function = 67
3.3.3 Identifying the Constraints = 67
3.3.4 Complete Formulation and Solution of the Diet Problem of Mountain View General Hospital = 68
3.4 Linear Programming Models for Portfolio Management = 69
3.4.1 Identifying the Decision Variables = 70
3.4.2 Identifying the Objective Function = 71
3.4.3 Identifying the Constraints = 71
3.4.4 Complete Formulation and Solution of the Investment Problem of Pension Planners, Inc. = 73
3.5 Linear Programming Models for Blending Problems = 74
3.5.1 Identifying the Decision Variables = 75
3.5.2 Identifying the Objective Function = 75
3.5.3 Identifying the Constraints = 75
3.5.4 Complete Formulation and Solution of the Blending Problem fo Hexxon Oil Company = 77
3.6 Linear Programming Models for Aggregate Production Planning = 78
3.6.1 Identifying the Decision Variables = 79
3.6.2 Identifying the Objective Function = 80
3.6.3 Identifying the Constraints = 82
3.6.4 Complete Formulation and Solution of the Production-Planning Problem of NSC = 85
Case Study = 86
The Problem of American Steel Company = 87
Mathematical Formulation = 88
Mathematical Formulation of the Problem of American Steel Company = 95
Summary = 97
Exercises = 98
Critical-Thinking Projects : Problem Formulations = 103
A : The Blending Problem of Hexxon Oil Company = 103
B : The Production Problem of ASA Steel company = 104
C : The Delivery Problem of Hexxon Oil Company = 105
D : The Delivery Problem of Gasahol, Inc. = 106
E : The Data-Transmission Problem of Tele Comm = 108
Video Case : Hot Dog for Linear Programming = 109
CHAPTER 4 LINEAR PROGRAMMING : THE GRAPHICAL APROACH = 110
4.1 The Geometry of a Linear Program with Two Variables = 111
4.1.1 Graphing the Constraints of a Linear Program = 112
4.1.2 Using the Objective Function to Obtain an Optimal Solution = 114
4.1.3 Obtaining Numerical Values for the Optimal Solution = 117
4.2 Linear Programs Having Special Geometric Properties = 119
4.2.1 Infeasible Linear Programs = 119
4.2.2 Unbounded Linear Programs = 121
4.2.3 Linear Programs With Redundant Constraints = 124
4.2.4 Linear Programs With Alternate Optimal Solutions = 125
4.3 A Graphical Approach to Sensitivity and Parametric Analysis = 125
4.3.1 Sensitivity Analysis of the Objective Function Coefficients = 126
4.3.2 Sensitivity Analysis of the Right-Hand-Side Values = 132
4.3.3 Parametric Analysis of the Right-Hand-Side Values = 139
Additional Managerial Considerations = 146
Summary = 147
Appendix 4A : Review of Graphical Concepts in Two Dimensions = 148
4A.1 Drawing Straight Lines on a Graph = 148
4A.2 The Slope and Intercept of a Straight Line = 150
Exercises = 152
Video Case : Drawing Conclusions = 157
CHAPTER 5 LINEAR PROGRAMMING : A CONCEPTUAL APPROACH TO THE SIMPLEX ALGORITHM = 158
5.1 Why the Need for the Simplex Algorithm = 159
5.2 The General Finite-Improvement Algorithm = 160
5.3 The Geometric Finite-Improvement Algorithm for Linear Programs = 161
5.4 Standard Form = 163
5.4.1 An Example of Converting a Linear Program to Standard Form = 164
5.4.2 General Rules for Converting A Linear Program to Standard Form = 165
5.4.3 Summary of the Steps for Converting a Linear Program to Standard Form = 170
5.5 The Conceptual Steps of the Simplex Algorithm = 171
5.5.1 Basic Feasible Solutions : The Algebraic Definition of Extreme Points = 171
5.5.2 An Algebraic Finite-Improvement Algorithm for Linear Programs = 174
Summary = 177
Exercises = 178
Video Case : You Make the Call = 183
CHAPTER 6 LINEAR PROGRAMMING : USING THE COMPUTER = 184
6.1 The Example of Case Chemicals = 185
6.2 Interpreting the Optimal Solution = 186
6.2.1 Interpreting the Values of the Original Variables = 186
6.2.2 Interpreting the Values of the Slack Variables = 187
6.2.3 Interpreting the Output Pertaining to the Constraints = 188
6.3 Interpreting Sensitivity Output for Changes in One Parameter = 188
6.4 Using Sensitivity Output for Multiple Changes in the Parameters : The 100% Rule = 192
6.5 Interpreting Parametric Analysis Reports = 195
6.6 Computer Solution to Linear Programming Problems Using LINDO = 198
6.6.1 Problem Definition = 198
6.6.2 Problem Formulation = 199
6.6.3 Computer Solution = 199
6.6.4 Using Sensitivity and Parametric Reports = 200
6.7 Computer Solution to Linear Programming Problems Using EXCEL = 203
6.7.1 computer Solution = 203
6.7.2 Performing Sensitivity and Parametric Analysis = 205
Case Study = 208
Mathematical Formulation = 208
Computer Solution = 211
Using Sensitivity and Parametric Reports = 212
Additional Managerial Considerations = 218
Problems Associated with the Data = 218
Alternative Optimal Solutions = 219
Using Special Features of Software Packages = 219
Problems of Numerical Stability = 223
Summary = 224
Exercises = 224
Critical-Thinking Project A = 237
Critical-Thinking Project E = 237
Video Case : Keep on Trucking = 239
CHAPTER 7 MULTIOBJECTIVE OPTIMIZATION USING GOAL PROGRAMMING = 240
7.1 An Example of Multiobjective Optimization = 241
7.2 Goal Programming = 244
7.2.1 Identifying the Goals and Penalties = 245
7.2.2 The Linear Programming Formulation for a Goal-Programming Problem = 246
7.2.3 Computer Solution of the Goal-Programmming Problem of MTV Steel = 250
Case Study = 251
The Original Diet Problem of Mountain View General Hospital = 251
The Multiobjective Diet Problem of Mountain View General Hospital = 253
The Goal-Programming Approach for the Diet Problem of Moutain View General Hospital = 256
Using Penalties in the Constraints = 258
Additional Managerial Considerations = 261
Using Two-Sided Penalties = 261
Sensitivity Analysis for Goals and Penalties = 262
Alternative Aproaches for Muliobjective Optimization = 263
Summary = 265
Exercises = 266
Critical-Thinking Project D = 275
Video Case : We All Scream for Ice Cream-or Yogurt? = 276
CHAPTER 8 LINEAR INTEGER PROGRAMMING : APPLICATIONS AND ALGORITHMS = 277
8.1 Applications of Integer Programming Problems = 278
8.1.1 Personel Planning and Scheduling = 278
8.1.2 Capital Budgeting = 281
8.1.3 The Cutting-Stock Problem = 283
8.1.4 A Location Problem = 286
8.2 Linear Integer Programming : The Graphical Approach = 291
8.2.1 Solving Graphically an Integer Programming Problem with Two Variables = 291
8.2.2 The Problems with Rounding Noninteger Solutions = 293
8.3 Linear Integer Programming : A Conceptual Approach = 297
8.3.1 Listing the Possible Integer Solutions = 297
8.3.2 Linear Programming Problems Associated with the Nodes of the Tree = 299
8.3.3 The Branch-and-Bound Method : A Conceptual Approach = 301
8.3.4 Solving the Problem of BUDD Using the Branch-and-Bound Method = 305
8.4 The Branch-and-Bound Method : A Mathematical Approach = 308
8.4.1 Properties of Linear Programming Relaxation Problems = 308
8.4.2 The Branch-and-Bound Algorithm = 312
8.5 Solving Mixed Linear Integer Programming Problems = 320
8.5.1 Formulating the Expansion Problem of Case Chemicals = 321
8.5.2 Solution to the Mixed Integer Problem of Case Chemicals = 323
8.6 Linear Integer Programming : Using the Computer = 326
8.6.1 Computer Analysis of the Personnel-Planning Model for Burlington Bank = 327
8.6.2 Computer Solution to the Capital-Budgeting Problem of High-Tech = 328
8.6.3 Computer Solution to the Cutting-Stock Problem of Spiral Paper, Inc. = 329
8.6.4 Computer Solution to the Location Problem of Cosmic Computer Company = 331
Case Study = 333
The Problem of Scheduling Crews for Commuter Airways = 334
Problem formulation = 335
Computer Solution = 338
Additional Managerial Considerations = 340
Care When Formulating an Ingeter Programming Problem = 340
Care When Solving an Integer Programming Problem = 341
Using Specialized Algorithms = 341
Using Heuristic Algorithms = 342
Summary = 343
Exercises = 343
Critical-Thinking Project B = 351
Video Case : Tyranny of Choices = 352
CHAPTER 9 DISTRIBUTION NETWORK PROBLEMS : TRANSPORTATION, TRANSSHIPMENT, AND ASSIGNMENT PROBLEMS = 353
9.1 What Is a Destribution Network? = 354
9.1.1 An Example of a Distributio Network Problem = 354
9.1.2 Network Representation of a Destribution Problem = 355
9.1.3 Mathemartical Formulation of a Destribution Network Problem = 356
9.1.4 Solving Distribution Network Problems = 360
9.2 The Transportation Problem = 361
9.3 The Transportation Algorithm : A Conceptual Approach = 363
9.3.1 Converting a Transportation Problem from Unbalanced to Balanced = 363
9.3.2 The transportation Tableau = 367
9.3.3 The Properties of an Optimal Shipping Plan = 369
9.3.4 A Finite-Improvement Algorithm for the Transportation Problem : A Conceptual Approach = 373
9.3.5 Comparing the Stepping Stone and Simplex Algorithms = 374
9.4 The Transportation Algorithm : Using the Computer = 375
9.5 The Transportation Problem : The Algebra of the Stepping Stone Algorithms = 380
9.5.1 Step 0 : Finding the Initial Feasible Shipping Plan = 382
9.5.2 Step 1 : Testing a Shipping Plan for Optimality = 386
9.5.3 Step 2 : Moving to an Improved Shipping Plan = 391
9.6 Variations of the Transportation Problem = 396
9.6.1 Incorporating Additional Costs and / or Profits = 396
9.6.2 The Capacitated Transportation Problem = 399
9.6.3 Prohibited Routes = 399
9.6.4 Lower Bounds on Supplies and / or Demands = 401
9.7 The General Network Distribution Problem : The Transshipment Problem = 402
9.7.1 The Transshipment Algorithm : A Conceptual Approach = 402
9.7.2 The Transshipment Algorithm : Using the Computer = 403
9.7.3 Summary of the Transshipment Problem = 407
9.8 The Assignment Problem = 407
9.8.1 Network and Mathematical Representation of an Assignment Problem = 408
9.8.2 The Assignment Algorithm = 413
Case Study = 421
The Warehouse-Location Problem of Good Tire, Inc. = 421
Identifying an Approach to Solving the Warehouse-Location Problem of Good Tire, Inc = 421
Solving the Waregousr-Location Problem of Good Tire, Inc = 423
Additional Managerial Considerations = 425
Summary = 425
Appendix 9A : Computing Reduced Costs by the MODI Method = 426
Appendix 9B : Resolving Degeneracy in the Transportation Algorithm = 428
9B.1 Resolving Degeneracy in the Matrix Minimum Method = 428
9B.2 Resolving Degeneracy in the Stepping Stone Algorithm = 433
Appendix 9C : The Traveling-Salesperson Problem = 433
9C.1 Applications of The Traveling-Salesperson Problem = 434
9C.2 The Traveling-Salesperson Problem : Some Heuristics = 437
Exercises = 444
Critical-Thinking Project C = 453
Video Case : Packing Them In = 454
CHAPTER 10 PROJECT MANAGEMENT : CPM AND PERT = 455
10.1 Developing the Project Network = 456
10.1.1 Identifying Individual Tasks = 456
10.1.2 Obtaining Time Estimates for Each Task = 458
10.1.3 Creating the Precedence Table for the Project = 458
10.1.4 Drawing the Project Network = 459
10.1.5 The Project Network for the Project of Period Publishing Company = 466
10.2 Project Management Using Deterministic Times (CPM) = 468
10.2.1 Computing the Project-Completion Time = 468
10.2.2 Indentifying Critical Tasks = 474
10.3 Project Management with Deterministic Task Times : Using the Computer = 481
10.4 Expediting a Project Using Crashing Techniques = 483
10.4.1 Obtaining Additional Cost Data for the Tasks = 483
10.4.2 Developing the Crashing Model = 485
10.4.3 Solving the Crashing Model = 489
10.5 Project Management Using Probabilstic Task Times (PERT) = 491
10.5.1 Listing the Tasks, Identifying the Precedence Relationship, and Drawing the Project Network = 492
10.5.2 Estimating Task-Completion Times = 493
10.5.3 Computing the Expected Project-Completion Time = 494
10.5.4 Probabilistic Analysis of the Project-Completion Time = 495
Case Study = 500
Problem Description and Formulation = 500
Solution Procedure = 501
Managerial Questions and Analysis = 502
Additional Managerial Considerations = 510
Using CPM and PRET to Monitor a Project = 510
Relying on Assumptions = 512
Summary = 512
Appendix 10A : The Activity-on-Node Representation of a Project Network = 512
10A.1 Drawing the Project Network = 513
Exercises = 516
Critical-Thinking Project F = 524
Video Case : Prepare for Departure = 526
PART 2 STOCHASTIC MODELS
CHAPTER 11 DECISION ANALYSIS = 527
11.1 Single-Level Decision Making = 529
11.1.1 Problem Formulation = 529
11.1.2 Making the Decision = 530
11.2 Expected Value of Perfect Information = 535
11.3 Experted Value of Sample Information = 536
11.3.1 Designing and Conducting the Market Research = 537
11.3.2 Revising the Probabilities Based on the Market Research = 538
11.3.3 Identifying the Optimal Decision Based on the Revised Probabilities = 514
11.3.4 Computing the Expected Value of the Sample Information = 542
11.4 Single-Level Decision Problems : Using the Computer = 543
11.5 Decision Trees and Multilevel Decision Making = 547
11.5.1 The Decision Tree = 547
11.5.2 Multilevel Decision Making Using Decision Trees = 550
11.6 Decision Analysis : Using the Computer = 561
11.6.1 Single-Level Decision Using the Computer = 561
11.6.2 Multilevel Decision Using the Computer = 563
11.7 Decision Making Using Utility Theory = 565
11.7.1 Problem Description = 565
11.7.2 Selecting the Decision Using the Expected Profit = 567
11.7.3 Making the Decision Using Utility Theory = 567
11.7.4 Utility Functions = 569
Case Study = 571
Determining the Sensitivity Ranges of the Conditional Probabilities for High Investment = 572
Determining the Sensitivity Ranges of the Conditional probabilities for Moderate and Low Investment = 575
Additional Managerial Considerations = 577
Issus Related to the Problem Formulation = 577
Issues Related to the Computer Software = 577
Sensitivity Analysis = 578
Summary = 578
Exercises = 579
Critical-Thinking Project G = 585
Video Case : Decisions "On the Bubble" = 587
CHAPTER 12 INVERTORY MODELS = 588
12.1 Characteristics of Invertory Models = 590
12.1.1 Independent vs. Dependent Demand = 590
12.1.2 Deterministic vs. Probabilistic Demand = 590
12.1.3 Shortages = 591
12.1.4 Lead Times = 591
12.1.5 Quanity Discounts = 591
12.1.6 Ordering Policy = 591
12.2 Cost Components of an Inventory System = 592
12.2.1 The Ordering, or Setup, Cost (K) = 592
12.2.2 Thr Purchase Cost (C) = 592
12.2.3 The Holding Cost (H) = 593
12.2.4 The Shortage Cost (B) = 593
12.3 The Economic-Order-Quanity (EOQ) Inventory Model = 594
12.3.1 An Example of an EOQ Problem = 595
12.3.2 Computing the Optimal Order Quantity = 595
12.3.3 Determining the Reorder Point = 600
12.3.4 The EOQ Model : Using the Computer = 601
12.4 The Economic-Order-Quantity Model with Quantity Discounts = 604
12.4.1 An Example of an EOQ Problem with Quantity Discounts = 604
12.4.2 Computing the Optimal Order Quantity = 605
12.5 The Production-Order-Quantity (POQ) Inventory Model = 608
12.5.1 An Example of a POQ Problem = 609
12.5.2 Computing the Optimal Order Quantity = 610
12.5.3 Determining the Reorder Point = 614
12.5.4 The POQ Model : Using the Computer = 618
12.6 Invertory Systems with Probabilistic Demand : The Continuous-Review Model = 620
12.6.1 Computing the Order Quantity (Q) and the Reorder Point (R) = 621
12.6.2 Computing the Amount of Safety Stock to Meet a Service Level = 622
12.6.3 The Probabilistic EOQ Model : Using the Computer = 625
12.6.4 Summary = 627
12.7 Inventory Systems With Probabilistic Demand : The Periodic-Review Model = 628
12.7.1 The Periodic-Review Problem of Suburban Hospital = 629
12.7.2 Determining the Periodic-Review Policy = 629
12.7.3 Computing the Cost of the Periodic-Review Policy = 631
12.7.4 Periodic-Review Policy When the Lead Time (L) Exceeds the Review Period (T) = 632
Case Study = 633
Problem Description = 633
Problem Analysis = 634
Analysis of the Combined Ordering Policy = 634
Postoptimality Analysis = 636
Additional Managerial Considerations = 638
Sensitivity Analysis = 638
The ABC Classification = 639
Other Inventory Model = 639
Just-in-Time Inventory Management = 640
Dependent Demand : Materials Requirement Planning (MRP) = 640
Identifying the Appropriate Invertory Model = 642
Information Systems for Invertory Control = 642
Summary = 643
Appendix 12A : Derivation of the Optimal EOQ and POQ Formulas = 644
Formulas = 644
12A.1 The Order Quantity for the EOQ Model = 645
12A.2 The Order Quantity for the POQ Model = 646
Exercises = 646
Critical-Thinking Project H = 651
Video Case : Just in Time = 653
CHAPTER 13 QUEUEING MODELS = 654
13.1 Characteristics of a Queueing System = 656
13.1.1 The Customer Population = 657
13.1.2 The Arrival Process = 657
13.1.3 The Queueing Process = 659
13.1.4 The Service Process = 659
13.1.5 Classifications of Queueing Models = 661
13.2 Performance Measures for Evaluating a Queueing System = 662
13.2.1 Some Common Performance Measures = 663
13.2.2 Relationships Among Performance Measures = 664
13.3 Analyzing a Single-Line Single-Channel Queueing System with Exponential Arrival and Service Processes (M / M / 1) = 666
13.3.1 Computing the Performance Measure = 667
13.3.2 Interpreting the Performance Measures = 669
13.4 Analyzing a Single-Line, Multiple-Channel Queueing System with Exponential Arrival and Service Processes (M / M / c) = 671
13.4.1 Computing the Performance Measures = 672
13.5 Economic Analysis of Queueing Systems = 678
13.5.1 Modeling and Analyzing the Current Queueing System = 678
13.5.2 Cost Analysis of the Queueing System = 679
13.6 Analysis of Other Queueing Models Using the Computer = 681
13.6.1 An M / M / c System with a Finite Customer Population (M / M / c / K) = 681
13.6.2 An M / M / c System with Limited Waiting Capacity (M / M / c / K) = 684
13.6.3 A Queueing Systme with a General Service-Time Distribution (M / G / c) = 687
Additional Managerial Considerations = 689
Choosing an Appropriate Model = 689
Additonal Queueing Systems = 690
Sensitivity Analysis = 691
Trade-Off Analysis = 691
Summary = 692
Exercises = 693
Critical-Thinking Project Ⅰ : The Queueing Problem of Texas Airways = 697
Video Case : A Visit to Disney = 699
CHAPTER 14 COMPUTER SIMULATION : THE GENERAL METHODOLOGY = 700
14.1 The Basic Concept of Computer Simulation = 701
14.1.1 A First Example of Computer Simulation = 701
14.1.2 A Second Example of Computer Simulation = 705
14.2 Advantages and Disadvantages of Computer Simulation = 709
14.3 The Computer-Simulation Methodology = 710
14.3.1 Classifying the System = 710
14.3.2 Identifying the Components of a Computer Simulation = 711
14.3.3 Designing the Computer Simulation = 713
14.3.4 Generating Random Numbers = 714
14.4 A Simulation of a Bus Stop = 720
14.4.1 Problem Description = 720
14.4.2 Designing the Simulation = 720
14.4.3 Generating Random Numbers = 721
14.4.4 Designing the Bookkeeping Scheme = 721
14.4.5 Obtaining the Final Statistics = 725
Additional Managerial Considerations = 729
Data Collection = 729
Statistical Aspects of Sumulation = 730
Summary = 730
Appendix 14A : Using 0-1 Random Numbers to Obtain Random Numbers from a Given Distribution = 731
Exercises = 732
Critical-Thinking Project J : The Satellite Communication Problem of Tele Comm : Part Ⅰ = 735
Video Case : Chrysler Takes a New Step = 737
CHAPTER 15 COMPUTER SIMULATION : APPLICATIONS AND STATISTICAL ANALYSIS = 738
15.1 A Financial Simulation = 739
15.1.1 Problem Description = 739
15.1.2 Designing the Simulation = 739
15.1.3 Generating Random Numbers = 740
15.1.4 Designing the Bookkeeping Scheme = 741
15.2 A Simulation of an Inventory Problem = 747
15.2.1 Problem Description = 747
15.2.2 Identifying the Class of Inventory Policies = 748
15.2.3 Designing the Simulation = 748
15.2.4 Generating Random Numbers = 749
15.2.5 Designing the Bookkeeping Schmem = 750
15.3 A Simulation of a Queueing Problem = 775
15.3.1 Problem Description = 755
15.3.2 Designing the Simulation = 756
15.3.3 Generating Random Numbers = 757
15.3.4 Designing the Bookkeeping Schmem = 760
15.3.5 Obtaining the Final Statistics = 765
15.4 Simulation Software = 770
15.4.1 An Example of Simulating with the Software Package @RISK = 771
15.4.2 An Example of Simulating with the Software Package SIMAN = 774
15.5 Ststistical Analysis of Simulation Output = 782
15.5.1 Determining the Sample Size for Estimating a Mean Value = 783
15.5.2 Determining the Sample Size for Estimating a Proportion = 785
Additional Managerial Considerations = 788
Validation = 788
Statistical Aspects of Simulation = 789
Computational Concerns = 790
Summary = 790
Exercisis = 791
Critical-Thinking Project K : The Satellite Communication Problem of Tele Comm : Part Ⅱ = 795
Video Case : Virtual Realities = 798
CHAPTER 16 FORECASTING = 799
16.1 Classification of Time-Series Models = 800
16.1.1 Level Models = 801
16.1.2 Trend Models = 802
16.1.3 Seasonal Models = 804
16.1.4 Trend-Seasonal Models = 805
16.2 Performance Measures for Evaluating Forecasting Models = 807
16.2.1 Root-Mean-Square Error (RMSE) = 808
16.2.2 Mean Absolute Percent Error (MAE) = 809
16.2.3 Using the RMSE to Create a Confidence Interval for Future Demands = 810
16.2.4 Bias of a Forecasting Model = 811
16.3 Developing Using a Level Model for Forecasting = 812
16.3.1 The Method of Moving Averages = 813
16.3.2 Exponential Smoothing = 817
16.3.3 Comparing Moving Averages and Exponential Smoothing = 823
16.3.4 Forecasting with a Level Model = 823
16.4 Developing and Using a Trend Model for Forecasting = 824
16.4.1 The Method of Linear Regression = 826
16.4.2 The Method of Exponential Smoothing in Trend Models = 828
16.4.3 Forecasting with a Trend Model = 834
16.5 Developing and Using a Seasonal Model for Forecasting = 835
16.5.1 An Example of a Seasonal Model = 836
16.5.2 Using the Computer to Develop a Seasonal Model = 837
16.5.3 Using a Seasonal Model for Forecasting = 839
16.6 Developing and Using a Trend-Seasonal Model for Forecsting = 840
16.6.1 Using the Computer to Develop a Trend-Seasonal Model = 841
16.6.2 Using a Trend-Seasonal Model for Forecasting = 844
16.7 Forecasting Using Causal Factors = 845
16.7.1 Bulding and Using a Causal Forecasting Model = 847
Additional Managerial Considerations = 849
Choice of a Forecasting Model = 849
Availability of Historical Data = 849
Model Validation = 849
Short-Range Versus Long-Range Forecasts = 850
Monitoring and Updating the Model = 850
Summary = 850
Exercises = 851
Critical-Thinking Projcet L : The Forecasting Problem of American Auto Parts = 856
Video Case : Going Up? Going Down? = 858
APPENDIX A UNFORM 0-1 RANDOM NUMBERS = 859
APPENDIX B STATISTICAL TABLES = 862
ANSWERS TO SELECTED EXERCISES = 865
INDEX = 897
