Adaptive Business Intelligence

Contents
Part I: Complex Business Problems
1 Introduction....................................... 3
2 Characteristics of Complex Business Problems............................... 9
2.1 Number of Possible Solutions................................ 10
2.2 Time-Changing Environment ......................... 12
2.3 Problem-Specific Constraints ........................ 13
2.4 Multi-objective Problems ........................... 14
2.5 Modeling the Problem.......................................... 16
2.6 A Real-World Example .......................... 19
3 An Extended Example: Car Distribution.................. 25
3.1 Basic Terminology..................................... 25
3.2 Off-lease Cars .......................................... 27
3.3 The Problem .......................................... 28
3.4 Transportation.......................... 30
3.5 Volume Effect............................... 32
3.6 Price Depreciation and Inventory......................... 33
3.7 Dynamic Market Changes ............................. 33
3.8 The Solution .................................. 34
4 Adaptive Business Intelligence................................ 37
4.1 Data Mining.................................. 38
4.2 Prediction........................................... 41
4.3 Optimization ............................................................................... 43
4.4 Adaptability ................................................................................ 44
4.5 The Structure of an Adaptive Business Intelligence System.......... 45
Part II: Prediction and Optimization
5 Prediction Methods and Models .......................................................... 49
5.1 Data Preparation.......................................................................... 51
5.2 Different Prediction Methods ...................................................... 56
5.2.1 Mathematical Methods .................................................... 565.2.2 Distance Methods............................................................ 62
5.2.3 Logic Methods ................................................................ 64
5.2.4 Modern Heuristic Methods .............................................. 68
5.2.5 Additional Considerations ............................................... 69
5.3 Evaluation of Models .................................................................. 69
5.4 Recommended Reading............................................................... 74
6 Modern Optimization Techniques....................................................... 75
6.1 Overview .................................................................................... 75
6.2 Local Optimization Techniques ................................................... 82
6.3 Stochastic Hill Climber ............................................................... 87
6.4 Simulated Annealing................................................................... 90
6.5 Tabu Search ................................................................................ 96
6.6 Evolutionary Algorithms............................................................ 101
6.7 Constraint Handling ................................................................... 108
6.8 Additional Issues........................................................................ 112
6.9 Recommended Reading.............................................................. 114
7 Fuzzy Logic ......................................................................................... 117
7.1 Overview ................................................................................... 119
7.2 Fuzzifier .................................................................................... 119
7.3 Inference System........................................................................ 123
7.4 Defuzzifier ................................................................................. 127
7.5 Tuning the Membership Functions and Rule Base....................... 128
7.6 Recommended Reading.............................................................. 129
8 Artificial Neural Networks ................................................................. 131
8.1 Overview ................................................................................... 132
8.2 Node Input and Output ............................................................... 134
8.3 Different Types of Networks ...................................................... 136
8.3.1 Feed-Forward Neural Networks...................................... 137
8.3.2 Recurrent Neural Networks ............................................ 140
8.4 Learning Methods ...................................................................... 142
8.4.1 Supervised Learning....................................................... 142
8.4.2 Unsupervised Learning................................................... 146
8.5 Data Representation ................................................................... 147
8.6 Recommended Reading.............................................................. 148
9 Other Methods and Techniques.......................................................... 151
9.1 Genetic Programming................................................................. 151
9.2 Ant Systems and Swarm Intelligence.......................................... 158
9.3 Agent-Based Modeling............................................................... 163
9.4 Co-evolution .............................................................................. 169
9.5 Recommended Reading.............................................................. 173
Part III: Adaptive Business Intelligence
10 Hybrid Systems and Adaptability....................................................... 177
10.1 Hybrid Systems for Prediction.................................................... 178
10.2 Hybrid Systems for Optimization ............................................... 183
10.3 Adaptability ............................................................................... 187
11 Car Distribution System ..................................................................... 191
11.1 Overview ...................................................... 192
11.2 Graphical User Interface................................. 194
11.2.1 Constraint Handling .................................. 195
11.2.2 Reporting.................................................... 201
11.3 Prediction Module.............................................. 203
11.4 Optimization Module ........................................... 206
11.5 Adaptability Module ............................................ 208
11.6 Validation ............................................... 211
12 Applying Adaptive Business Intelligence....................................... 215
12.1 Marketing Campaigns .......................................... 215
12.2 Manufacturing................................................... 221
12.3 Investment Strategies ......................................... 224
12.4 Emergency Response Services................................. 228
12.5 Credit Card Fraud..................................................... 232
13 Conclusion........................................................ 239
Index ................................................................. 243

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