Decision Support System. Tools and Techniques

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