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Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
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Searching... | 30000010119030 | HD30.2 M52 2007 | Open Access Book | Book | Searching... |
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Summary
Summary
In the modern information era, managers must recognize the competitive opportunities represented by decision-support tools. Adaptive business intelligence systems combine prediction and optimization techniques to assist decision makers in complex, rapidly changing environments. These systems address the fundamental questions: What is likely to happen in the future? And what is the best decision right now? Adaptive Business Intelligence includes elements of data mining, predictive modeling, forecasting, optimization, and adaptability.
The authors have considerable academic research backgrounds in artificial intelligence and related fields, combined with years of practical consulting experience in businesses and industries worldwide. In this book they explain the science and application of numerous prediction and optimization techniques, as well as how these concepts can be used to develop adaptive systems. The techniques covered include linear regression, time-series forecasting, decision trees and tables, artificial neural networks, genetic programming, fuzzy systems, genetic algorithms, simulated annealing, tabu search, ant systems, and agent-based modeling.
This book is suitable for business and IT managers who make decisions in complex industrial and service environments, nonspecialists who want to understand the science behind better predictions and decisions, and students and researchers who need a quick introduction to this field.
Table of Contents
Part I Complex Business Problems | |
1 Introduction | p. 3 |
2 Characteristics of Complex Business Problems | p. 9 |
2.1 Number of Possible Solutions | p. 10 |
2.2 Time-Changing Environment | p. 12 |
2.3 Problem-Specific Constraints | p. 13 |
2.4 Multi-objective Problems | p. 14 |
2.5 Modeling the Problem | p. 16 |
2.6 A Real-World Example | p. 19 |
3 An Extended Example: Car Distribution | p. 25 |
3.1 Basic Terminology | p. 25 |
3.2 Off-lease Cars | p. 27 |
3.3 The Problem | p. 28 |
3.4 Transportation | p. 30 |
3.5 Volume Effect | p. 32 |
3.6 Price Depreciation and Inventory | p. 33 |
3.7 Dynamic Market Changes | p. 33 |
3.8 The Solution | p. 34 |
4 Adaptive Business Intelligence | p. 37 |
4.1 Data Mining | p. 38 |
4.2 Prediction | p. 41 |
4.3 Optimization | p. 43 |
4.4 Adaptability | p. 44 |
4.5 The Structure of an Adaptive Business Intelligence System | p. 45 |
Part II Prediction and Optimization | |
5 Prediction Methods and Models | p. 49 |
5.1 Data Preparation | p. 51 |
5.2 Different Prediction Methods | p. 56 |
5.2.1 Mathematical Methods | p. 56 |
5.2.2 Distance Methods | p. 62 |
5.2.3 Logic Methods | p. 64 |
5.2.4 Modern Heuristic Methods | p. 68 |
5.2.5 Additional Considerations | p. 69 |
5.3 Evaluation of Models | p. 69 |
5.4 Recommended Reading | p. 74 |
6 Modern Optimization Techniques | p. 75 |
6.1 Overview | p. 75 |
6.2 Local Optimization Techniques | p. 82 |
6.3 Stochastic Hill Climber | p. 87 |
6.4 Simulated Annealing | p. 90 |
6.5 Tabu Search | p. 96 |
6.6 Evolutionary Algorithms | p. 101 |
6.7 Constraint Handling | p. 108 |
6.8 Additional Issues | p. 112 |
6.9 Recommended Reading | p. 114 |
7 Fuzzy Logic | p. 117 |
7.1 Overview | p. 119 |
7.2 Fuzzifier | p. 119 |
7.3 Inference System | p. 123 |
7.4 Defuzzifier | p. 127 |
7.5 Tuning the Membership Functions and Rule Base | p. 128 |
7.6 Recommended Reading | p. 129 |
8 Artificial Neural Networks | p. 131 |
8.1 Overview | p. 132 |
8.2 Node Input and Output | p. 134 |
8.3 Different Types of Networks | p. 136 |
8.3.1 Feed-Forward Neural Networks | p. 137 |
8.3.2 Recurrent Neural Networks | p. 140 |
8.4 Learning Methods | p. 142 |
8.4.1 Supervised Learning | p. 142 |
8.4.2 Unsupervised Learning | p. 146 |
8.5 Data Representation | p. 147 |
8.6 Recommended Reading | p. 148 |
9 Other Methods and Techniques | p. 151 |
9.1 Genetic Programming | p. 151 |
9.2 Ant Systems and Swarm Intelligence | p. 158 |
9.3 Agent-Based Modeling | p. 163 |
9.4 Co-evolution | p. 169 |
9.5 Recommended Reading | p. 173 |
Part III Adaptive Business Intelligence | |
10 Hybrid Systems and Adaptability | p. 177 |
10.1 Hybrid Systems for Prediction | p. 178 |
10.2 Hybrid Systems for Optimization | p. 183 |
10.3 Adaptability | p. 187 |
11 Car Distribution System | p. 191 |
11.1 Overview | p. 192 |
11.2 Graphical User Interface | p. 194 |
11.2.1 Constraint Handling | p. 195 |
11.2.2 Reporting | p. 201 |
11.3 Prediction Module | p. 203 |
11.4 Optimization Module | p. 206 |
11.5 Adaptability Module | p. 208 |
11.6 Validation | p. 211 |
12 Applying Adaptive Business Intelligence | p. 215 |
12.1 Marketing Campaigns | p. 215 |
12.2 Manufacturing | p. 221 |
12.3 Investment Strategies | p. 224 |
12.4 Emergency Response Services | p. 228 |
12.5 Credit Card Fraud | p. 232 |
13 Conclusion | p. 239 |
Index | p. 243 |