Quantitative business modeling:
Gespeichert in:
| Beteiligte Personen: | , , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Mason, Ohio
South-Western
c 2002
|
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017429357&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | XXIV, 454 S. Ill., graph. Darst. |
| ISBN: | 032401600X |
Internformat
MARC
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| 100 | 1 | |a Meredith, Jack R. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Quantitative business modeling |c Jack R. Meredith ; Scott M. Shafer ; Efraim Turban |
| 264 | 1 | |a Mason, Ohio |b South-Western |c c 2002 | |
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Datensatz im Suchindex
| _version_ | 1819265558096379904 |
|---|---|
| adam_text | Preface xvii
About the Authors xxiii
Chapter I
Decision Making and Quantitative
Modeling I
Chapter 2
Data Collection and Analysis 38
Chapter 3
Statistical Models: Regression
and Forecasting 97
Chapter 4
Optimization and Mathematical
Programming 148
Chapter 5
Decision Analysis 221
Chapter 6
Queuing Theory 279
Chapter 7
Simulation 317
Chapter 8
Implementation and Project
Management 372
Appendix A
Mathematics 435
Appendix B
Tables 441
Index 451
Contents
Preface xvii
About the Authors xxiii
Chapter I
Decision Making and Quantitative Modeling I
1.1 Quantitative Business Modeling 7
Definition of a Model 9 Benefits and Drawbacks
of Modeling 10 Types of Models 11 Effective
Modelers 14
1.2 The Modeling Process 14
A Five-Step Modeling Process 16 Step 1:
Opportunity/Problem Recognition 17 Step 2: Model
Formulation 17 Step 3: Data Collection 21 Step 4:
Analysis of the Model 23 Step 5: Implementation and
Project Management 25
1.3 Detailed Modeling Example 28
Step 1: Opportunity/Problem Recognition 28 Step 2:
Model Formulation 29 Step 3: Data Collection 30
Step 4: Analysis of the Model 30 Step 5: Implementation
and Project Management 30
viii Contents
1.4 Software for Modeling 33
Questions 33 Experiential Exercises 34 Modeling
Exercises 35 Case: Henry Ford Hospital 36 Endnotes 37
Bibliography 37
Chapter 2
Data Collection and Analysis 38
2.1 Data Collection 39
2.2 Summarizing Data 42
Descriptive Statistics 42 Statistical Displays 44
2.3 Probability and Random Variables 47
Subjective Probability 48 Logical Probability 48
Experimental Probability 48 Event Relationships and
Probability Laws 48 Probability Distributions 51
2.4 Common Probability Distributions 52
The Binomial Distribution 53 The Poisson
Distribution 54 The Exponential Distribution 55
The Normal Distribution 56 The t Distribution 58
2.5 Distributions of Sample Statistics 58
2.6 Chi-Square Goodness of Fit Test 60
2.7 Point and Interval Estimation 64
Interval Estimation of a Mean 65 Determining the Size
of the Sample for a Normal Distribution 68 Interval
Estimation and Determination of Sample Size for
a Proportion 69
2.8 Hypothesis Testing 71
Hypothesis Tests for Means 73 Comparing Multiple Means—
Analysis of Variance (ANOVA) 77
Contents ix
2.9 Detailed Modeling Example 81
Step 1: Opportunity/Problem Recognition 81 Step 2: Model
Formulation 82 Step 3: Data Collection 82 Step 4:
Analysis of the Model 85 Step 5: Implementation 86
Questions 89 Experiential Exercise 89 Modeling
Exercises 90 Case: Fiberease Inc. 93 Case: InterAccess Inc. 95
Case: eApp Inc. 95 Endnote 96 Bibliography 96
Chapter 3
Statistical Models: Regression
and Forecasting 97
3.1 The Modeling Process for Statistical Studies 99
3.2 The Simple Linear Regression Model 100
Calculating the Regression Model Parameters 103
The Coefficient of Determination and the Correlation
Coefficient 105 Regression Analysis Assumptions 109
Using the Regression Model 110
3.3 The Multiple Regression Model 112
3.4 Developing Regression Models 115
Step 1: Identify Candidate Independent Variables to Include in
the Model 115 Step 2: Transform the Data 117 Step 3:
Select the Variables to Include in the Model 118 Step 4:
Analyze the Residuals 118
3.5 Regression Hypothesis Tests 119
3.6 Time Series Analysis 121
Components of a Time Series 121 Time Series Models 123
3.7 Detailed Modeling Example 130
Step 1: Opportunity/Problem Recognition 130 Step 2: Model
Formulation 131 Step 3: Data Collection 131 Step 4:
Analysis of the Model 131 Step 5: Implementation 135
x Contents
Questions 140 Experiential Exercise 140 Modeling
Exercises 141 Case: Resale Value of Long s Automobile 144
Case: Lewisville Crate Company 144 Bibliography 147
Chapter 4
Optimization and Mathematical
Programming 148
4.1 The Modeling Process for Optimization Studies 153
Optimization 153 The Modeling Process 154 Structure
of the Chapter 156
4.2 Linear Programming 156
The Output-Mix Problem 157 The Blending Problem 157
Formulating the Linear Programming Model 157 Output-
Mix and Blending Problems: Two Examples 158 Example:
The Blending (Minimization) Problem 160 The General LP
Model 161 Advantages, Assumptions, and Solution
Methods 162 Distribution Problems: Transportation,
Transshipment, Assignment 164
43 Analysis of the Model by the Graphical Method 165
Example 1: A Maximization Problem 165 Example 2: A
Minimization Problem 172 Utilization of the Resources—
Slack and Surplus Variables 174 Special Situations 175
4.4 Solving Linear Programming Models with Excel 177
Using Excel s Solver 177 Solving Large Problems 181
Back to Startron s Dilemma 185
4.5 Sensitivity ( What-If ) Analysis 189
Why a Sensitivity Analysis? 189 Sensitivity Analysis:
Objective Function 190 Sensitivity Analysis: Right-Hand
Sides 192 Sensitivity Analysis with Excel 192
4.6 Integer Programming 196
Overview of Integer Programming 196 Example: Southern
General Hospital 197 The Zero-One Model 200
Example: The Fixed-Charge Situation 201
Contents xi
4.7 Detailed Modeling Example 203
Step 1: Opportunity/Problem Recognition 203 Step 2: Model
Formulation 203 Step 3: Data Collection 203 Step 4:
Analysis of the Model 205 Step 5: Implementation 208
Questions 210 Experiential Exercise 211 Modeling
Exercises 211 Case: The Daphne Jewelry Company 217 Case:
Hensley Valve Corp. (A) 219 Case: Hensley Valve Corp. (B) 219
Bibliography 220
Chapter 5
Decision Analysis 221
5.1 The Modeling Process for Decision Analysis Studies 222
The Modeling Process 223 Structure of the Chapter 224
5.2 The Decision Analysis Situation 224
Mary s Dilemma 224 The Structure of Decision
Tables 225 Classification of Decision Situations 228
5.3 Decisions Under Certainty 228
Complete Enumeration 229 Example: Assignment of
Employees to Machines 229 Computation with Analytical
Models 230
5.4 Decisions Under Uncertainty 230
Equal Probabilities (Laplace) Criterion 231 Pessimism
(Maximin or Minimax) Criterion 231 Optimism (Maximax
or Minimin) Criterion 232 Coefficient of Optimism
(Hurwicz) Criterion 233 Regret (Savage) Criterion 237
5.5 Decisions Under Risk 237
Objective and Subjective Probabilities 238 Solution
Procedures to Decision Making Under Risk 238 Notes on
Implementation 242 Sensitivity Analysis 242
5.6 Decision Trees for Risk Analysis 243
Structure of a Decision Tree 243 Evaluating a Decision
Tree 245 The Multiperiod, Sequential Decision Case 246
xii Contents
5.7 The Value of Additional Information 250
Information Quality: Perfect Versus Imperfect
Information 250 The Value of Perfect Information 251
5.8 Imperfect Information and Bayes Theorem 253
Bayes Theorem 253 Using Revised Probabilities with
Imperfect Information 254 Calculating Revised
Probabilities 259 Computing the Revised Probabilities 260
5.9 Detailed Modeling Example 262
Step 1: Opportunity/Problem Recognition 262
Step 2: Model Formulation 262 Step 3: Data
Collection 263 Step 4: Analysis of the Model 263
Step 5: Implementation 265
Questions 270 Experiential Exercises 270 Modeling
Exercises 271 Case: Maintaining the Water Valves 276
Case: The Air Force Contract 277 Endnotes 278
Bibliography 278
Chapter 6
Queuing Theory 279
6.1 The Modeling Process for Queuing Studies 282
Step 1: Opportunity/Problem Recognition 282
Step 2: Model Formulation 282 Step 3: Data
Collection 283 Step 4: Analysis of the Model 283
Step 5: Implementation 283
6.2 The Queuing Situation 284
Characteristics of Waiting Line Situations 284 The
Structure of a Queuing System 285 The Managerial
Problem 286 The Costs Involved in a Queuing Situation
287
6.3 Modeling Queues 288
Queuing Model Notation 288 Deterministic Queuing
Systems 289 The Arrival Process 290 The Service
Process 292 Measures for the Service 293 The Waiting
Line 294
Contents xiii
6.4 Analysis of the Basic Queue (M/M/l FCFS/°°/°°) 295
Poisson-Exponential Model Characteristics 295 Measure of
Performance (Operating Characteristics) 296 Managerial
Use of the Measures of Performance 298 Using Excels
Goal Seek Function 298
6.5 More Complex Queuing Situations 298
Multifacility Queuing Systems (M/M/K FCFS/W°°) 299
Example: Multichannel Queue 301 Example:
Multichannel Queue at Macro-Market 301 Serial
(Multiphase) Queues 304 Example: Serial Queue—
Three-Station Process 304
6.6 Detailed Modeling Example 306
Step 1: Opportunity/Problem Recognition 306
Step 2: Model Formulation 306 Step 3: Data
Collection 306 Step 4: Analysis of the Model 307
Step 5: Implementation 308
Questions 309 Experiential Exercise 310 Modeling
Exercises 310 Case: City of Help 315 Case: Newtown
Maintenance Division 315 Bibliography 316
Chapter 7
Simulation 317
7.1 General Overview of Simulation 319
Types of Simulation 320 Uses of Simulation 322
Advantages and Disadvantages of Simulation 322
7.2 The Modeling Process for Monte Carlo Simulation 323
Step 1: Opportunity/Problem Recognition 323
Step 2: Model Formulation 323 Step 3: Data
Collection 324 Step 4: Analysis of the Model 324
Step 5: Implementation 327
7.3 The Monte Carlo Methodology 327
The Tourist Information Center 327 Simulation
Terminology 328 Generating Random Variates in the
Monte Carlo Process 330
xiv Contents
7.4 Time Independent, Discrete Simulation 332
Example: Marvin s Service Station 333 Solution by
Simulation 333
7.5 Time Dependent Simulation 339
Simulation Analysis with Discrete Distributions 240
Simulation with Continuous Probability Distributions 342
7.6 Risk Analysis 342
7.7 Detailed Modeling Example 344
Step 1: Opportunity/Problem Recognition 344 Steps 2
and 3: Model Formulation and Data Collection 344
Step 4: Analysis of the Model 347 Step 5:
Implementation 348
Appendix: Crystal Ball 2000 350
Questions 350 Experiential Exercise 359 Modeling
Exercises 360 Case: Medford Delivery Service 366 Case:
Warren Lynch s Retirement 366 Case: Cartron, Inc. 369
Endnotes 371 Bibliography 371
Chapter 8
Implementation and Project Management 372
8.1 Implementation and Project Modeling 373
The Project Modeling Process 373 Structure of the
Chapter 374
8.2 Implementing the Modeling Study 375
Soft Aspects 375 Rational Issues and Reconsideration 377
The Role of Project Management 378 Example: Moose
Lake 378
8.3 Planning the Project 381
Step 1: Analysis of the Project 382 Step 2: Sequence
the Activities 382 Step 3: Estimate Activity Times and
Costs 382
Contents xv
8.4 Scheduling the Project 383
Step 4: Construct the Network 383 Step 5: Event
Analysis 385 PERT/CPM Network Characteristics 391
Estimating Activity Times in PERT 393 Finding the
Probabilities of Completion in PERT 394 Example: Finding
the Probability of Completion within a Desired Time, D 397
Example: Finding the Duration Associated with a Desired
Probability 399 Determining the Distribution of Project
Completion Times with Simulation 399
8.5 Step 6: Monitoring and Controlling the Project 403
Monitoring the Project 403 Controlling the Project 403
Example: Resource Allocation Schedule 405 Critical Path
Method (CPM): Cost-Time Trade-Offs 406 Example:
Finding the Least-Cost Plan 409 Example: Least-Cost
Plan for 22 Days 441 Analyzing Cost-Time Trade-Offs
with Excel s Solver 314
8.6 Detailed Modeling Example 418
Step 1: Opportunity/Problem Recognition 418 Step 2: Model
Formulation 418 Step 3: Data Collection 421 Step 4:
Analysis of the Model 423 Step 5: Implementation 424
Questions 426 Experiential Exercise 426 Modeling
Exercises 426 Case: NutriTech 431 Case: Dart
Investments 432 Bibliography 433
Appendix A
Mathematics 435
Appendix B
Tables 441
Index 451
|
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| id | DE-604.BV023787149 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T13:34:06Z |
| institution | BVB |
| isbn | 032401600X |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-017429357 |
| oclc_num | 915878211 |
| open_access_boolean | |
| owner | DE-634 |
| owner_facet | DE-634 |
| physical | XXIV, 454 S. Ill., graph. Darst. |
| publishDate | 2002 |
| publishDateSearch | 2002 |
| publishDateSort | 2002 |
| publisher | South-Western |
| record_format | marc |
| spellingShingle | Meredith, Jack R. Shafer, Scott M. Turban, Efraim 1930- Quantitative business modeling Entscheidungsmodell (DE-588)4121201-0 gnd Management (DE-588)4037278-9 gnd Operations Research (DE-588)4043586-6 gnd |
| subject_GND | (DE-588)4121201-0 (DE-588)4037278-9 (DE-588)4043586-6 |
| title | Quantitative business modeling |
| title_auth | Quantitative business modeling |
| title_exact_search | Quantitative business modeling |
| title_full | Quantitative business modeling Jack R. Meredith ; Scott M. Shafer ; Efraim Turban |
| title_fullStr | Quantitative business modeling Jack R. Meredith ; Scott M. Shafer ; Efraim Turban |
| title_full_unstemmed | Quantitative business modeling Jack R. Meredith ; Scott M. Shafer ; Efraim Turban |
| title_short | Quantitative business modeling |
| title_sort | quantitative business modeling |
| topic | Entscheidungsmodell (DE-588)4121201-0 gnd Management (DE-588)4037278-9 gnd Operations Research (DE-588)4043586-6 gnd |
| topic_facet | Entscheidungsmodell Management Operations Research |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017429357&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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