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Data, Models and Decisions

Fundamentals of Management Science

By: Dimitris Bertsimas, Robert Freund

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Hardcover | 1 August 1997

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The book combines topics from two traditionally distinct quantitative subjects: probability/statistics and optimization models, into one unified treatment of quantitative methods and models for management and business. The book stresses those fundamental concepts that are most important for the practical analysis of management decisions: modeling and evaluating uncertainty explicitly, understanding the dynamic nature of decision-making, using historical data and limited information effectively, simulating complex systems, and allocating scarce resources optimally.

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Non-Fiction Business & Management Business Mathematics & Systems Management & Management Techniques Computing & I.T.Computer Science Economics

Decision Analysis	p. 1
A Decision Tree Model and its Analysis	p. 2
Summary of the General Method of Decision Analysis	p. 16
Another Decision Tree Model and its Analysis	p. 17
The Need for a Systematic Theory of Probability	p. 30
Further Issues and Concluding Remarks on Decision Analysis	p. 33
Case Modules	p. 35
Kendall Crab and Lobster, Inc.	p. 35
Buying a House	p. 38
The Acquisition of DSOFT	p. 38
National Realty Investment Corporation	p. 39
Exercises	p. 44
Fundamentals of Discrete Probability	p. 49
Outcomes, Probabilities and Events	p. 50
The Laws of Probability	p. 51
Working with Probabilities and Probability Tables	p. 54
Random Variables	p. 65
Discrete Probability Distributions	p. 66
The Binomial Distribution	p. 67
Summary Measures of Probability Distributions	p. 72
Linear Functions of a Random Variable	p. 79
Covariance and Correlation	p. 82
Joint Probability Distributions and Independence	p. 86
Sums of Two Random Variables	p. 88
Some Advanced Methods in Probability*	p. 91
Summary	p. 96
Case Modules	p. 97
Arizona Instrumentation, Inc. and the Economic Development Board of Singapore	p. 97
San Carlos Mud Slides	p. 98
Graphic Corporation	p. 99
Exercises	p. 100
Continuous Probability Distributions and Their Applications	p. 111
Continuous Random Variables	p. 111
The Probability Density Function	p. 112
The Cumulative Distribution Function	p. 115
The Normal Distribution	p. 120
Computing Probabilities for the Normal Distribution	p. 127
Sums of Normally Distributed Random Variables	p. 132
The Central Limit Theorem	p. 135
Summary	p. 139
Exercises	p. 139
Statistical Sampling	p. 147
Random Samples	p. 148
Statistics of a Random Sample	p. 150
Confidence Intervals for the Mean, for Large Sample Size	p. 161
The t-Distribution	p. 165
Confidence Intervals for the Mean, for Small Sample Size	p. 166
Estimation and Confidence Intervals for the Population Proportion	p. 169
Experimental Design	p. 174
Comparing Estimates of the Mean of Two Distributions	p. 178
Comparing Estimates of the Population Proportion of Two Populations	p. 180
Summary and Extensions	p. 182
Case Modules	p. 183
Consumer Convenience, Inc.	p. 183
Posidon, Inc.	p. 184
Housing Prices in Lexington, Massachusetts	p. 185
Scallop Sampling	p. 185
Exercises	p. 189
Simulation Modeling: Concepts and Practice	p. 195
A Simple Problem: Operations at Conley Fisheries	p. 196
Preliminary Analysis of Conley Fisheries	p. 197
A Simulation Model of the Conley Fisheries Problem	p. 199
Random Number Generators	p. 201
Creating Numbers that Obey a Discrete Probability Distribution	p. 203
Creating Numbers that Obey a Continuous Probability Distribution	p. 205
Completing the Simulation Model of Conley Fisheries	p. 211
Using the Sample Data for Analysis	p. 213
Summary of Simulation Modeling, and Guidelines on the Use of Simulation	p. 217
Computer Software for Simulation Modeling	p. 217
Typical Uses of Simulation Models	p. 218
Case Modules	p. 219
The Gentle Lentil Restaurant	p. 219
To Hedge or not to Hedge?	p. 223
Ontario Gateway	p. 228
Casterbridge Bank	p. 235
Regression Models: Concepts and Practice	p. 245
Prediction Based on Simple Linear Regression	p. 246
Prediction Based on Multiple Linear Regression	p. 253
Using Spreadsheet Software for Linear Regression	p. 258
Interpretation of Computer Output of a Linear Regression Model	p. 259
Sample Correlation and R[superscript 2] in Simple Linear Regression	p. 271
Validating the Regression Model	p. 274
Warnings and Issues in Linear Regression Modeling	p. 279
Regression Modeling Techniques	p. 283
Illustration of the Regression Modeling Process	p. 288
Summary and Conclusions	p. 294
Case Modules	p. 295
Predicting Heating Oil Consumption at OILPLUS	p. 295
Executive Compensation	p. 297
The Construction Department at Croq'Pain	p. 299
Sloan Investors, Part I	p. 306
Exercises	p. 313
Linear Optimization	p. 323
Formulating a Management Problem as a Linear Optimization Model	p. 324
Key Concepts and Definitions	p. 332
Solution of a Linear Optimization Model	p. 335
Creating and Solving a Linear Optimization Model in a Spreadsheet	p. 347
Sensitivity Analysis and Shadow Prices on Constraints	p. 354
Guidelines for Constructing and Using Linear Optimization Models	p. 365
Linear Optimization Under Uncertainty*	p. 367
A Brief Historical Sketch of the Development of Linear Optimization	p. 374
Case Modules	p. 375
Short-Run Manufacturing Problems at DEC	p. 375
Sytech International	p. 380
Filatoi Riuniti	p. 389
Exercises	p. 397
Nonlinear Optimization	p. 411
Formulating a Management Problem as a Nonlinear Optimization Model	p. 412
Graphical Analysis of Nonlinear Optimization Models in Two Variables	p. 420
Computer Solution of Nonlinear Optimization Problems	p. 425
Shadow Prices Information in Nonlinear Optimization Models	p. 428
A Closer Look at Portfolio Optimization	p. 431
Taxonomy of the Solvability of Nonlinear Optimization Problems*	p. 432
Case Modules	p. 436
Endurance Investors	p. 436
Capacity Investment, Marketing, and Production at ILG, Inc.	p. 442
Exercises	p. 444
Discrete Optimization	p. 451
Formulating a Management Problem as a Discrete Optimization Model	p. 452
Graphical Analysis of Discrete Optimization Models in Two Variables	p. 461
Computer Solution of Discrete Optimization Problems	p. 464
The Branch-and-Bound Method for Solving a Discrete Optimization Model*	p. 468
Summary	p. 471
Case Modules	p. 471
International Industries, Inc.	p. 471
Supply Chain Management at Dellmar, Inc.	p. 474
The National Basketball Dream Team	p. 476
Exercises	p. 478
Intergration in the Art of Decision Modeling	p. 485
Management Science Models in the Airline Industry	p. 486
Management Science Models in the Investment Management Industry	p. 496
A Year in the Life of a Manufacturing Company	p. 498
Summary	p. 501
Case Modules	p. 502
Sloan Investors, Part II	p. 502
Revenue Management at Atlantic Air	p. 503
A Strategic Alliance for the Lexington Laser Corporation	p. 508
Yield of a Multi-step Manufacturing Process	p. 510
Prediction of Yields in Manufacturing	p. 512
Allocation of Production Personnel	p. 513
Appendix	p. 517
References	p. 521
Index	p. 525
Table of Contents provided by Syndetics. All Rights Reserved.

Data, Models and Decisions

Fundamentals of Management Science

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