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GLOBALEDITIONQuantitative Analysisfor ManagementTHIRTEENTH EDITIONBarry Render • Ralph M. Stair, Jr. • Michael E. Hanna • Trevor S. Hale
- Page 2 and 3: T H I R T E E N T H E D I T I O NG
- Page 4 and 5: About the AuthorsBarry Render is Pr
- Page 6 and 7: Brief ContentsCHAPTER 1 Introductio
- Page 8 and 9: CONTENTS 7CHAPTER 3 Decision Analys
- Page 10 and 11: CONTENTS 9Diet Problems 323Ingredie
- Page 12 and 13: CONTENTS 11APPENDIX D F Distributio
- Page 14 and 15: PrefaceOverviewWelcome to the thirt
- Page 16 and 17: PREFACE 15Chapter 5 Forecasting. A
- Page 18 and 19: PREFACE 17Instructor’s Solutions
- Page 20 and 21: CHAPTER1Introduction to Quantitativ
- Page 22 and 23: 1.3 The Quantitative Analysis Appro
- Page 24 and 25: 1.3 The Quantitative Analysis Appro
- Page 26 and 27: 1.4 How to Develop a Quantitative A
- Page 28 and 29: 1.5 THE ROLE OF COMPUTERS AND SPREA
- Page 30 and 31: 1.5 The Role of Computers and Sprea
- Page 32 and 33: 1.6 Possible Problems in the Quanti
- Page 34 and 35: 1.7 Implementation—Not Just the F
- Page 36 and 37: SELF-TEST 35Parameter A measurable
- Page 38 and 39: CASE STUDY 371-20 Farris Billiard S
- Page 40 and 41: CHAPTER2Probability Concepts and Ap
- Page 42 and 43: 2.1 Fundamental Concepts 41TABLE 2.
- Page 44 and 45: 2.1 Fundamental Concepts 43Unions a
- Page 46 and 47: 2.2 Revising Probabilities with Bay
- Page 48 and 49: 2.3 Further Probability Revisions 4
- Page 50 and 51: 2.4 Random Variables 49TABLE 2.5 Ex
- Page 52 and 53:
2.5 Probability Distributions 51FIG
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2.6 The Binomial Distribution 53FIG
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2.6 The Binomial Distribution 55FIG
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2.7 The Normal Distribution 57FIGUR
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2.7 The Normal Distribution 59STEP
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2.7 The Normal Distribution 61FIGUR
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2.8 THE F DISTRIBUTION 63FIGURE 2.1
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2.9 The Exponential Distribution 65
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2.10 The Poisson Distribution 67whe
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KEY EQUATIONS 69Normal Distribution
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solved Problems 71Strongly agree 40
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DISCUSSION QUESTIONS AND PROBLEMS 7
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DISCUSSION QUESTIONS AND PROBLEMS 7
- Page 78 and 79:
DISCUSSION QUESTIONS AND PROBLEMS 7
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Appendix 2.1: Derivation of Bayes
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CHAPTER3Decision AnalysisLEARNING O
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3.3 DECISION MAKING UNDER UNCERTAIN
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3.3 Decision Making Under Uncertain
- Page 88 and 89:
3.4 Decision Making Under Risk 87IN
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3.4 Decision Making Under Risk 89TA
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3.4 Decision Making Under Risk 91FI
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3.5 Using Software for Payoff Table
- Page 96 and 97:
3.6 Decision Trees 95PROGRAM 3.2BKe
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3.6 Decision Trees 97FIGURE 3.4Larg
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3.6 Decision Trees 99EMV calculatio
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3.7 How Probability Values Are Esti
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3.7 How Probability Values Are Esti
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3.8 Utility Theory 105FIGURE 3.7Sta
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3.8 Utility Theory 107FIGURE 3.10Pr
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GLOSSARY 109SummaryTherefore, alt
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soLVED PROBLEMS 111ALTERNATIVEGOODM
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soLVED PROBLEMS 113Solved Problem 3
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From this information we can comput
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DISCUSSION QUESTIONS AND PROBLEMS
- Page 120 and 121:
DISCUSSION QUESTIONS AND PROBLEMS
- Page 122 and 123:
DISCUSSION QUESTIONS AND PROBLEMS
- Page 124 and 125:
DISCUSSION QUESTIONS AND PROBLEMS
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CASE STUDY 125Case StudyStarting
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CASE STUDY 127FIGURE 3.15Master
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CHAPTER4Regression ModelsLEARNING O
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4.2 Simple Linear Regression 1314.2
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4.3 Measuring the Fit of the Regres
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4.4 Assumptions of the Regression M
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4.5 Testing the Model for Significa
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4.5 Testing the Model for Significa
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4.6 Using Computer Software for Reg
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4.6 Using Computer Software for Reg
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4.7 Multiple Regression Analysis 14
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4.8 Binary or Dummy Variables 147Tw
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4.10 Nonlinear Regression 149A vari
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4.10 Nonlinear Regression 151FIGURE
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153GLOSSARY A significant F valu
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solved Problems 155Solved ProblemsS
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DISCUSSION QUESTIONS AND PROBLEMS 1
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DISCUSSION QUESTIONS AND PROBLEMS 1
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DISCUSSION QUESTIONS AND PROBLEMS 1
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Appendix 4.1: Formulas for Regressi
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CHAPTER5ForecastingLEARNING OBJECTI
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5.2 Components of a Time-Series 167
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5.3 Measures of Forecast Accuracy 1
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5.3 Measures of Forecast Accuracy 1
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5.4 Forecasting Models—Random Var
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5.4 Forecasting Models—Random Var
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5.4 Forecasting Models—Random Var
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5.5 Forecasting Models—Trend and
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5.5 Forecasting Models—Trend and
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5.6 Adjusting for Seasonal Variatio
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5.7 Forecasting Models—Trend, Sea
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5.7 Forecasting Models—Trend, Sea
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5.7 Forecasting Models—Trend, Sea
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5.8 Monitoring and Controlling Fore
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KEY EQUATIONS 193Deseasonalized Dat
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SELF-TEST 195andY n = predicted q
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DISCUSSION QUESTIONS AND PROBLEMS
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DISCUSSION QUESTIONS AND PROBLEMS
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201 CASE STUDY Case StudyForecas
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CHAPTER6Inventory Control ModelsLEA
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6.2 Inventory Decisions 205Resource
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6.3 Economic Order Quantity: Determ
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6.3 Economic Order Quantity: Determ
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6.3 Economic Order Quantity: Determ
- Page 214 and 215:
6.4 Reorder Point: Determining When
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6.5 EOQ Without the Instantaneous R
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6.5 EOQ Without the Instantaneous R
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6.6 Quantity Discount Models 219FIG
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6.7 Use of Safety Stock 221PROGRAM
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6.7 Use of Safety Stock 223How is t
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6.7 Use of Safety Stock 225From App
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6.8 Single-Period Inventory Models
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6.8 Single-Period Inventory Models
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6.8 Single-Period Inventory Models
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6.10 Dependent Demand: The Case for
- Page 236 and 237:
6.10 Dependent Demand: The Case for
- Page 238 and 239:
6.11 Just-In-Time Inventory Control
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GLOSSARY 239Summarynot only the fun
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sOLVED PROBLEMS 241Solved ProblemsS
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SELF-TEST 243Solved Problem 6-4The
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DISCUSSION QUESTIONS AND PROBLEMS 2
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DISCUSSION QUESTIONS AND PROBLEMS
- Page 250 and 251:
DISCUSSION QUESTIONS AND PROBLEMS
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DISCUSSION QUESTIONS AND PROBLEMS
- Page 254 and 255:
Appendix 6.1: Inventory Control wit
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CHAPTER7Linear Programming Models:G
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7.2 Formulating LP Problems 257TABL
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7.3 Graphical Solution to an LP Pro
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7.3 Graphical Solution to an LP Pro
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7.3 Graphical Solution to an LP Pro
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7.3 Graphical Solution to an LP Pro
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7.3 Graphical Solution to an LP Pro
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7.4 SOLVING FLAIR FURNITURE’S LP
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7.4 Solving Flair Furniture’s LP
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7.4 Solving Flair Furniture’s LP
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7.5 Solving Minimization Problems 2
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7.5 Solving Minimization Problems 2
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7.6 Four Special Cases in LP 279PRO
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7.6 Four Special Cases in LP 281FIG
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7.7 Sensitivity Analysis 283affect
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7.7 Sensitivity Analysis 285PROGRAM
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7.7 Sensitivity Analysis 287FIGURE
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7.7 Sensitivity Analysis 289FIGURE
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solved Problems 291Solved ProblemsS
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solved Problems 293Solved Problem 7
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SELF-TEST 295Self-Test●●●●
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dISCUSSION QUESTIONS AND PROBLEMS 2
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dISCUSSION QUESTIONS AND PROBLEMS 2
- Page 302 and 303:
dISCUSSION QUESTIONS AND PROBLEMS 3
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dISCUSSION QUESTIONS AND PROBLEMS 3
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dISCUSSION QUESTIONS AND PROBLEMS 3
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CHAPTER8Linear Programming Applicat
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8.1 Marketing Applications 309PROGR
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8.2 Manufacturing Applications 311P
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8.2 Manufacturing Applications 313P
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8.2 Manufacturing Applications 315I
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8.3 Employee Scheduling Application
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8.4 Financial Applications 319PROGR
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8.4 Financial Applications 321PROGR
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8.5 Ingredient Blending Application
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8.5 Ingredient Blending Application
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8.6 Other Linear Programming Applic
- Page 330 and 331:
Problems 3294. What is the objectiv
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Problems 331TYPE OF ADCOSTPER ADAUD
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Problems 333DEVICEhours each week.
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Problems 335(c) Are the laboratorie
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CHAPTER9Transportation, Assignment,
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9.1 The Transportation Problem 339w
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9.1 The Transportation Problem 341L
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9.2 The Assignment Problem 343PROGR
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9.3 The Transshipment Problem 345PR
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9.3 The Transshipment Problem 347Th
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9.4 Maximal-Flow Problem 349FIGURE
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9.5 Shortest-Route Problem 351start
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9.6 Minimal-Spanning Tree Problem 3
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SUMMARY 355IN ACTIONFacility Locati
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Solved Problems 357The computer out
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Discussion Questions and Problems 3
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discussion Questions and Problems 3
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discussion Questions and Problems 3
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discussion Questions and Problems 3
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discussion Questions and Problems 3
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discussion Questions and Problems 3
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Case Study 371TABLE 9.6Andrew-Carte
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APPENDIX 9.1: USING QM FOR WINDOWS
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CHAPTER10Integer Programming, Goal
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10.1 Integer Programming 377FIGURE
- Page 380 and 381:
10.1 Integer Programming 379PROGRAM
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10.2 Modeling with 0-1 (Binary) Var
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10.2 Modeling with 0-1 (Binary) Var
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10.2 Modeling with 0-1 (Binary) Var
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10.3 Goal Programming 387IN ACTIONC
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10.3 Goal Programming 389A key idea
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10.4 Nonlinear Programming 391Unlik
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Glossary 393To solve this nonlinear
- Page 396 and 397:
Solved Problems 395whereX 1 = numbe
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Discussion Questions and Problems 3
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Discussion Questions and Problems 3
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Discussion Questions and Problems 4
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BIBLIOGRAPHY 403Case StudyOakton Ri
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CHAPTER11Project ManagementLEARNING
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11.1 PERT/CPM 40711.1 PERT/CPMQuest
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11.1 PERT/CPM 409FIGURE 11.1Network
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11.1 PERT/CPM 4113. Latest start ti
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11.1 PERT/CPM 413IN ACTIONProject M
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11.1 PERT/CPM 415We know that the s
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11.1 PERT/CPM 417wish to see a Gant
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11.2 PERT/Cost 419FIGURE 11.9Gantt
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11.2 PERT/Cost 421TABLE 11.7 Budget
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11.3 Project Crashing 423IN ACTIONS
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11.3 Project Crashing 425There are
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11.3 Project Crashing 427For activi
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KEY EQUATIONS 429Backward Pass A pr
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SOLVED PROBLEMS 431Solved Problem 1
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DISCUSSION QUESTIONS AND PROBLEMS
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435 DISCUSSION QUESTIONS AND PRO
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437 DISCUSSION QUESTIONS AND PRO
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439 DISCUSSION QUESTIONS AND PRO
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441 CASE STUDY Case StudyFamily
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443 APPENDIX 11.1: PROJECT MANAG
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CHAPTER12Waiting Lines and Queuing
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12.2 Characteristics of a Queuing S
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12.2 Characteristics of a Queuing S
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12.2 Characteristics of a Queuing S
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12.3 SINGLE-CHANNEL QUEUING MODEL W
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12.3 SINGLE-CHANNEL QUEUING MODEL W
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12.4 MULTICHANNEL QUEUING MODEL WIT
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12.4 MULTICHANNEL QUEUING MODEL WIT
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12.6 FINITE POPULATION MODEL (M/M/1
- Page 464 and 465:
12.7 Some General Operating Charact
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KEY EQUATIONS 465GlossaryBalking Th
- Page 468 and 469:
solved Problems 467Solved ProblemsS
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Self-Test 469Solved Problem 12-4Vac
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DISCUSSION QUESTIONS AND PROBLEMS
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DISCUSSION QUESTIONS AND PROBLEMS
- Page 476 and 477:
CASE STUDY 475with an average of
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BIBLIOGRAPHY 477Case StudyWinter
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CHAPTER13Simulation ModelingLEARNIN
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13.2 Monte Carlo Simulation 481HIST
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13.2 Monte Carlo Simulation 483Prob
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13.2 Monte Carlo Simulation 485TABL
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13.2 Monte Carlo Simulation 487PROG
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13.3 Simulation and Inventory Analy
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13.3 Simulation and Inventory Analy
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13.3 Simulation and Inventory Analy
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13.4 Simulation of a Queuing Proble
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13.5 Simulation Model for a Mainten
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13.5 Simulation Model for a Mainten
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13.5 Simulation Model for a Mainten
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13.6 Other Simulation Issues 503Eco
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SOLVED PROBLEMS 505Solved ProblemsS
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SELF-TEST 507(1)CUSTOMERNUMBER(2)RA
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509diSCUSSION QUESTIONS AND PROB
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511diSCUSSION QUESTIONS AND PROB
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513 DISCUSSION QUESTIONS AND PRO
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515 CASE STUDY TABLE 13.15 Incom
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517 CASE STUDY FIGURE 13.6Temper
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CHAPTER14Markov AnalysisLEARNING OB
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14.1 States and State Probabilities
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14.3 Predicting Future Market Share
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14.5 Equilibrium Conditions 52514.5
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14.5 Equilibrium Conditions 527MODE
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14.6 Absorbing States and the Funda
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14.6 Absorbing States and the Funda
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solvED PROBLEMS 533Formula for c
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solvED PROBLEMS 535Solved Problem 1
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537 DISCUSSION QUESTIONS AND PRO
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539disCUSSION QUESTIONS AND PROB
- Page 542 and 543:
541 CASE STUDY 14-31 The followi
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543 APPENDIX 14.1: MARKOV ANALYS
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APPENDIX 14.2: MARKOV ANALYSIS WITH
- Page 548 and 549:
CHAPTER15Statistical Quality Contro
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15.2 Statistical Process Control 54
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15.3 Control Charts for Variables 5
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15.3 Control Charts for Variables 5
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15.4 Control Charts for Attributes
- Page 558 and 559:
15.4 CONTROL CHARTS FOR ATTRIBUTES
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KEY EQUATIONS 559PROGRAM 15.4Ex
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DISCUSSION QUESTIONS AND PROBLEMS
- Page 564 and 565:
DISCUSSION QUESTIONS AND PROBLEMS
- Page 566 and 567:
Appendix 15.1: Using QM for Windows
- Page 568 and 569:
AppendicesA. Areas Under the Standa
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Appendix A: Areas Under the Standar
- Page 572 and 573:
Appendix B: Binomial Probabilities
- Page 574 and 575:
Appendix B: Binomial Probabilities
- Page 576 and 577:
APPENDIX C: VALUES OF e -l FOR USE
- Page 578 and 579:
APPENDIX D: F DISTRIBUTION VALUES 5
- Page 580 and 581:
APPENDIX E: USING POM-QM FOR WINDOW
- Page 582 and 583:
APPENDIX F: USING EXCEL QM AND EXCE
- Page 584 and 585:
Appendix G: Solutions to Selected P
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Appendix G: Solutions to Selected P
- Page 588 and 589:
Appendix H: Solutions to Self-Tests
- Page 590 and 591:
Index0-1 (binary) variables, 381-38
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INDEX 591finding exponential probab
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INDEX 593Market shares, 375, 519, 5
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INDEX 595Random (R) component, of t
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INDEX 597Vertical axis, 130VLOOKUP
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GLOBALEDITIONFor these Global Editi