Decision Support System. Tools and Techniques

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Cover Half Title Title Page Copyright Page Dedication Table of Contents About the Author Preface Chapter 1 Introduction 1.1 Introduction 1.2 Classification of Decision Support System 1.3 Decision Support Tools 1.4 Overall Method of Decision-Making 1.5 Brief Introduction to Each Chapter in This Book Chapter 2 Decision Tree 2.1 Basic Concept 2.2 Algorithms for Construction of Decision Tree 2.2.1 ID3 2.2.1.1 Temperature 2.2.1.2 Humidity 2.2.1.3 Distance 2.2.1.4 Expense 2.2.1.5 Outlook 2.2.1.6 Temperature 2.2.1.7 Humidity 2.2.1.8 Distance 2.2.1.9 Expense 2.2.1.10 Outlook 2.2.2 C4.5 2.3 Time Complexity of Decision Tree 2.4 Various Applications of Decision Tree 2.5 Conclusion Reference Chapter 3 Decision Table 3.1 Basic Concept 3.1.1 Limited Entry Decision Table 3.1.2 Extended Entry Decision Table 3.1.3 Mixed Entry Decision Table 3.2 Approaches to Handle Inconsistency for Decision Tables 3.3 Decision Table Languages 3.3.1 Base Language 3.3.2 Rule Selection 3.3.3 Outer Language 3.4 Different Modifications of Decision Table and Latest Trend 3.5 Applications of Different Techniques on Decision Tables 3.6 Conclusion References Chapter 4 Predicate Logic 4.1 Introduction 4.2 Latest Research Studies on Predicate and Propositional Logic 4.3 Conclusion References Chapter 5 Fuzzy Theory and Fuzzy Logic 5.1 Basic Concepts 5.2 Fuzzification and Defuzzification 5.3 Some Advanced Fuzzy Sets 5.4 Conclusion References Chapter 6 Network Tools 6.1 Basic Concepts 6.2 Gantt Chart 6.3 Milestone Chart 6.4 Graphical Evaluation and Review Technique 6.5 Modifications of Traditional Tools 6.6 Conclusion References Chapter 7 Petri Net 7.1 Introduction 7.2 Different Types of Petri Nets 7.2.1 Autonomous Petri Net 7.2.2 State Graph 7.2.3 Event Graph 7.2.4 Conflict-Free Petri Net 7.2.5 Free-Choice Petri Net 7.2.6 Simple Petri Net 7.2.7 Pure Petri Net 7.2.8 Generalized Petri Net 7.2.9 Capacitated Petri Nets 7.2.10 Bounded Petri Net 7.2.11 Safe Petri Net 7.2.12 Colored Petri Net 7.2.13 Deadlock 7.3 Continuous and Hybrid Petri Nets 7.4 Basic Modeling Construct of Petri Net 7.5 Modifications of Different Types of Petri Nets and Latest Research Trends 7.6 Conclusion References Chapter 8 Markov Chain 8.1 Introduction 8.2 Transition Probability 8.2.1 Calculation of Transition Probability from the Current State 8.2.2 Calculation of Transition Probability from the Current State and the Previous State 8.2.3 Calculation of Multi-Step Transition Probability 8.3 Classification of Markov Chain 8.4 Some Other Miscellaneous Aspects 8.4.1 Canonical Form of Transition Matrix 8.4.2 Steady-State Probabilities for a Regular Markov Chain 8.5 Variations and Modifications of Markov Chains 8.6 Markov Chain Monte Carlo 8.6.1 Gibb's Sampling 8.7 Applications of Markov Chain 8.8 Conclusion Reference Chapter 9 Case-Based Reasoning 9.1 Introduction 9.2 Basic Elements and Basic Method 9.2.1 Similarity and Retrieval 9.2.2 CBR Tools 9.2.3 Case Presentation 9.3 Advanced Methods of CBR 9.4 Applications of Case-Based Reasoning and Latest Research 9.5 Conclusion References Chapter 10 Multi-Criteria Decision Analysis Techniques 10.1 Basic Concept 10. 2 Benchmark MCDA Techniques 10.2.1 TOPSIS 10.2.2 PROMETHEE 10.2.3 AHP 10.2.4 ANP 10.2.5 MAUT 10.2.6 MACBETH 10.2.7 MOORA 10.2.8 COPRAS 10.2.9 WASPAS 10.2.10 MABAC 10.3 Comparison Among MCDA Techniques 10.3.1 Theoretical Comparison 10.3.2 Rank Correlation Methods 10.3.3 A Newly Proposed Method 10.4 Modification of MCDA Techniques 10.5 Conclusion References Chapter 11 Some Other Tools 11.1 Introduction 11.2 Linear Programming 11.2.1 Simplex Method 11.2.1.1 Iteration 1 11.2.1.2 Iteration 2 11.2.1.3 Iteration 3 11.2.2 Two-Phase Method 11.2.2.1 Phase – I 11.2.2.2 First Iteration 11.2.2.3 Iteration 2 11.2.3 Big-M Method 11.2.3.1 First Iteration 11.2.3.2 Second Iteration 11.2.3.3 LPP with Unbounded Solution 11.2.4 Dual Simplex Method 11.2.5 Linear Fractional Programming 11.3 Simulation 11.3.1 Linear Congruential Generator (LCG) 11.3.2 Multiplicative Congruential Generator (MCG) 11.4 Big Data Analytics 11.5 Internet of Things 11.6 Conclusion References Chapter 12 Spatial Decision Support System 12.1 Introduction 12.2 Components of SDSS 12.3 SDSS Software 12.4 GRASS GIS Software 12.5 Conclusion References Chapter 13 Data Warehousing and Data Mining 13.1 Introduction 13.2 Data Warehouse 13.3 Data Mining 13.3.1 Process of Data Mining 13.3.2 Predictive Modeling 13.3.2.1 Linear Regression 13.3.3 Multiple Linear Regression 13.3.3.1 Assumptions for MLR 13.3.3.2 Linearity 13.3.3.3 Homoscedasticity 13.3.3.4 Uncorrelated Error Terms 13.3.3.5 Estimation of Model Parameters β 13.3.4 Quadratic Trend 13.3.4.1 Logarithmic Trend 13.3.4.2 Association Rules 13.3.4.3 Basic Concept 13.3.4.4 Support and Confidence 13.3.4.5 Association Rule Mining 13.3.4.6 Lift Measure 13.3.4.7 Sequence Rules 13.3.4.8 Segmentation 13.3.4.9 K-Means Clustering 13.3.4.10 Self-Organizing Maps 13.3.4.11 Database Segmentation 13.3.4.12 Clustering for Database Segmentation 13.3.4.13 Cluster Analysis: A Process Model (Figure 13.18) 13.4 Conclusion References Chapter 14 Intelligent Decision Support System 14.1 Introduction 14.2 Enterprise Information System 14.3 Knowledge Management 14.3.1 Concept Map 14.3.2 Semantic Network 14.4 Artificial Intelligence 14.4.1 Propositional Logic 14.4.2 Nature–Based Optimization Techniques 14.4.2.1 Genetic Algorithm 14.4.2.2 Particle Swarm Optimization 14.4.2.3 Ant Colony Optimization (ACO) 14.4.2.4 Artificial Immune Algorithm (AIA) 14.4.2.5 Differential Evolution (DE) 14.4.2.6 Simulated Annealing 14.4.2.7 Tabu Search 14.4.2.8 Gene Expression Programming 14.4.2.9 Frog Leaping Algorithm 14.4.2.10 Honey Bee Mating Algorithm (HBMA) 14.4.2.11 Bacteria Foraging Algorithm (BFA) 14.4.2.12 Cultural Algorithm (CA) 14.4.2.13 Firefly Algorithm (FA) 14.4.2.14 Cuckoo Search (CS) 14.4.2.15 Gravitational Search Algorithm (GSA) 14.4.2.16 Charged System Search 14.4.2.17 Intelligent Water Drops Algorithm 14.4.2.18 Bat Algorithm (BA) 14.4.2.19 Black Hole Algorithm (BHA) 14.4.2.20 Black Widow Optimization (BWO) Algorithm 14.4.2.21 Butterfly Optimization Algorithm (BOA) 14.4.2.22 Crow Search Algorithm (CSA) 14.4.2.23 Deer Hunting Optimization (DHO) Algorithm 14.4.2.24 Dragonfly Algorithm (DA) 14.4.2.25 Emperor Penguin Optimization (EPO) 14.4.2.26 Flower Pollination Algorithm (FPA) 14.4.2.27 Glowworm Swarm Based Optimization 14.4.2.28 Grasshopper Optimization Algorithm (GOA) 14.4.2.29 Grey Wolf Optimization (GWO) 14.4.2.30 Krill Herd Algorithm (KHA) 14.4.2.31 Lion Optimization Algorithm 14.4.2.32 Migratory Birds Optimization (MBO) 14.4.2.33 Moth-Flame Optimization Algorithm 14.4.2.34 Mouth-Brooding Fish Algorithm 14.4.2.35 Polar Bear Optimization Algorithm 14.4.2.36 Whale Optimization Algorithm (WOA) 14.4.2.37 Sea Lion Optimization Algorithm (SLOA) 14.4.2.38 Tarantula Mating-Based Strategy (TMS) 14.4.3 Some Latest Tools for Recent Applications of Artificial Intelligence 14.4.3.1 Cloud Computing 14.4.3.2 Big Data 14.5 Conclusion References Chapter 15 DSS Software 15.1 Introduction 15.2 Software Overview for DT 15.2.1 KNIME 15.3 Software Overview for Networking Techniques 15.4 Software Overview for Markov Process and Markov Chain 15.5 Software Overview for Regression 15.6 Software Overview for LP 15.7 Software Overview for Simulation 15.7.1 Create 15.7.2 Process 15.7.3 Decide 15.7.4 Dispose 15.7.5 Assign 15.8 Software Overview for Data Warehouse 15.9 Software Overview for Other Common Software 15.9.1 Matlab 15.9.2 C#.net 15.10 Conclusion Reference Chapter 16 Future of Decision Support System 16.1 Introduction 16.2 Conclusion References Index