This is truly an interdisciplinary book for knowledge workers in business, finance, management and socio-economic sciences based on fuzzy logic. It serves as a guide to and techniques for forecasting, decision making and evaluations in an environment involving uncertainty, vagueness, impression and subjectivity. Traditional modeling techniques, contrary to fuzzy logic, do not capture the nature of complex systems especially when humans are involved. Fuzzy logic uses human experience and judgement to facilitate plausible reasoning in order to reach a conclusion. Emphasis is on applications presented in the 27 case studies including Time Forecasting for Project Management, New Product Pricing, and Control of a Parasit-Pest System.
Author(s): Bojadziev G., Bojadziev M.
Year: 2007
Language: English
Pages: 232
Contents......Page 8
Foreword......Page 12
Preface to the Second Edition......Page 14
Preface to the First Edition......Page 16
List of Case Studies......Page 20
1.1 Classical Sets: Relations and Functions......Page 22
1.2 De nition of Fuzzy Sets......Page 30
1.3 Basic Operations on Fuzzy Sets......Page 36
1.4 Fuzzy Numbers......Page 40
1.5 Triangular Fuzzy Numbers......Page 43
1.6 Trapezoidal Fuzzy Numbers......Page 45
1.7 Fuzzy Relations......Page 47
1.8 Basic Operations on Fuzzy Relations......Page 50
1.9 Notes......Page 53
2.1 Basic Concepts of Classical Logic......Page 58
2.2 Many-Valued Logic......Page 62
2.3 What is Fuzzy Logic?......Page 64
2.4 Linguistic Variables......Page 65
2.5 Linguistic Modifiers......Page 67
2.6 Composition Rules for Fuzzy Propositions......Page 71
2.7 Semantic Entailment......Page 75
2.8 Notes......Page 77
3.1 Statistical Average......Page 82
3.2 Arithmetic Operations with Triangular and Trapezoidal Numbers......Page 83
3.3 Fuzzy Averaging......Page 87
3.4 Fuzzy Delphi Method for Forecasting......Page 92
3.5 Weighted Fuzzy Delphi Method......Page 97
3.6 Fuzzy PERT for Project Management......Page 98
3.7 Forecasting Demand......Page 108
3.8 Notes......Page 110
4 Decision Making in a Fuzzy Environment......Page 112
4.1 Decision Making by Intersection of Fuzzy Goals and Constraints......Page 113
4.2 Various Applications......Page 116
4.3 Pricing Models for New Products......Page 125
4.4 Fuzzy Averaging for Decision Making......Page 131
4.5 Multi-Expert Decision Making......Page 136
4.6 Fuzzy Zero-Based Budgeting......Page 140
4.7 Notes......Page 146
5.1 Introduction......Page 148
5.2 Modeling the Control Variables......Page 150
5.3 If ... and ... Then Rules......Page 154
5.4 Rule Evaluation......Page 157
5.5 Aggregation (Conict Resolution)......Page 159
5.6 Defuzzification......Page 165
5.7 Use of Singletons to Model Outputs......Page 170
5.8 Tuning of Fuzzy Logic Control Models......Page 171
5.9 One-Input–One-Output Control Model......Page 173
5.10 Notes......Page 176
6.1 Investment Advisory Models......Page 178
6.2 Fuzzy Logic Control for Pest Management......Page 185
6.3 Inventory Control Models......Page 191
6.4 Problem Analysis......Page 198
6.5 Potential Problem Analysis......Page 203
6.6 Notes......Page 206
7.1 Standard Relational Databases......Page 208
7.2 Fuzzy Queries......Page 211
7.3 Fuzzy Complex Queries......Page 217
7.4 Fuzzy Queries for Small Manufacturing Companies......Page 220
7.5 Fuzzy Queries for Stocks and Funds Databases......Page 227
7.6 Notes......Page 236
References......Page 238
Index......Page 244
