Note: Preface -- About This Book -- Contents -- Part I Computational Modelling -- 1 Computational Management-An Overview -- 1.1 Introduction -- 1.2 Computational Intelligence Techniques -- 1.2.1 Fuzzy Logic -- 1.2.2 Artificial Neural Networks -- 1.2.3 Evolutionary Computing -- 1.2.4 Learning Theories -- 1.2.5 Probabilistic Methods -- 1.3 Computational Aspects of Business Management -- 1.3.1 Marketing -- 1.3.2 Sales -- 1.3.3 Customer Relationship Management (CRM) -- 1.3.4 Finance -- 1.3.5 Human Resources -- 1.3.6 Manufacturing -- 1.3.7 Maintenance and Service -- 1.3.8 Research and Development -- 1.4 Benefits and Limitations of Computational Intelligence in Business -- 1.5 Applications of Computational Intelligence in Business -- 1.5.1 Applications of Computational Intelligence in the Field of Marketing -- 1.5.2 Computational Intelligence and Human Resource -- 1.5.3 Computational Intelligence and Finance -- 1.5.4 Computational Intelligence in Operations Management -- 1.6 Conclusion and Future Scope -- References -- 2 Mathematical and Computational Approaches for Stochastic Control of River Environment and Ecology: From Fisheries Viewpoint -- 2.1 Introduction -- 2.2 Stochastic Control -- 2.2.1 Stochastic Differential Equation -- 2.2.2 Performance Index and Value Function -- 2.2.3 HJB Equation -- 2.2.4 Remarks -- 2.3 Specific Problems -- 2.3.1 "Non-renewable" Fishery Resource Management -- 2.3.2 Dam-Reservoir System Management -- 2.3.3 Algae Growth Management -- 2.3.4 Sediment Storage Management -- 2.4 Coupled Problem -- 2.4.1 Overview -- 2.4.2 Control Problem -- 2.4.3 Numerical Scheme -- 2.4.4 Computational Conditions -- 2.4.5 Numerical Computation -- 2.5 Conclusions -- References -- 3 Models and Tools of Knowledge Acquisition -- 3.1 Introduction -- 3.1.1 Knowledge Economy -- 3.2 Literature Review -- 3.2.1 The Role and Importance of Knowledge Acquisition. , 3.2.2 Digitizing Knowledge Acquisition -- 3.3 Knowledge Management and Firm Strategy -- 3.3.1 Strategic Knowledge-An Internal and External Perspective -- 3.3.2 Competitive Intelligence -- 3.4 Knowledge Acquisition: Sources and Techniques -- 3.4.1 External Sources of Knowledge -- 3.4.2 Techniques of Knowledge Acquisition -- 3.5 Role of Technology in Knowledge Acquisition and Management -- 3.5.1 The Emergence of API Economy -- 3.5.2 Artificial Intelligence -- 3.6 Conclusion -- References -- 4 Profits Are in the Eyes of the Beholder: Entropy-Based Volatility Indicators and Portfolio Rotation Strategies -- 4.1 Introduction -- 4.2 Entropy and Its Applications to the VIX -- 4.3 Literature Review -- 4.4 Research Objectives -- 4.5 Theoretical Framework -- 4.6 Data Variables, Measurements, and Sources -- 4.7 Methods and Measures -- 4.8 Empirical Results -- 4.9 Formulation and Testing of Trading Strategies -- 4.10 Summary and Concluding Remarks -- Appendix 1: Trading Strategies Using India VIX Change and the Entropies (Both Approximate and Sample Entropies) Tested Through Simulations -- References -- 5 Asymmetric Spillovers Between the Stock Risk Series: Case of CESEE Stock Markets -- 5.1 Introduction -- 5.2 Related Literature Review -- 5.2.1 (M)GARCH Approaches to Modelling -- 5.2.2 Causality Testing and Cointegration -- 5.2.3 Spillover Methodology Approach -- 5.3 Methodology Description -- 5.3.1 VAR Models and Spillover Index -- 5.3.2 Asymmetric Spillovers -- 5.4 Empirical Results -- 5.4.1 Variable Description -- 5.4.2 Static Results -- 5.4.3 Dynamic Analysis -- 5.4.4 Robustness Checking -- 5.4.5 Simulation of Investment Strategies -- 5.5 Discussion and Conclusion -- Appendix -- References -- 6 Millennial Customers and Hangout Joints: An Empirical Study Using the Kano Quantitative Model -- 6.1 Introduction -- 6.1.1 Motivations of the Study. , 6.1.2 Background of the Study -- 6.2 Literature Review -- 6.3 Model Development -- 6.3.1 Research Gap -- 6.3.2 Research Objectives -- 6.3.3 The Kano Model -- 6.3.4 Research Design -- 6.3.5 Survey Instrument -- 6.4 Results and Discussion -- 6.4.1 Exploratory Factor Analysis Results -- 6.4.2 Reliability Analysis Results -- 6.4.3 Kano Quantitative Analysis Results -- 6.5 Conclusions and Future Scope -- 6.5.1 Conclusions -- 6.5.2 Future Scope -- References -- 7 The Effects of Oil Shocks on Macroeconomic Uncertainty: Evidence from a Large Panel Dataset of US States -- 7.1 Introduction -- 7.2 Data and Methodology -- 7.3 Results and Analysis -- 7.3.1 Linear Impulse Responses -- 7.3.2 Responses of Uncertainty Contingent on High- Versus Low-Oil Dependence States -- 7.3.3 Responses of Uncertainty at Different Horizons Conditioning on Aggregate US Uncertainty Spillovers -- 7.4 Conclusion -- Appendix -- References -- 8 Understanding and Predicting View Counts of YouTube Videos Using Epidemic Modelling Framework -- 8.1 Introduction -- 8.1.1 Literature Review -- 8.2 Building Block for Proposed Epidemic Modelling -- 8.2.1 Epidemic Modelling -- 8.2.2 Assumptions -- 8.2.3 Modelling Information Diffusion as Susceptibility Process -- 8.2.4 Modeling Viewing as Infection Process -- 8.2.5 Particular Case -- 8.3 Numerical Illustration -- 8.4 Results and Interpretation -- References -- 9 Gross Domestic Product Modeling Using ``Panel-Data'' Concept -- 9.1 Introduction -- 9.2 Materials -- 9.3 Statistical Preliminaries and Methods -- 9.4 Results -- 9.5 Concluding Remarks -- References -- 10 Supply Chain Scheduling Using an EOQ Model for a Two-Stage Trade Credit Financing with Dynamic Demand -- 10.1 Introduction -- 10.2 Literature Review -- 10.3 Assumptions and Notations -- 10.3.1 Assumptions: -- 10.3.2 Notations -- 10.4 Mathematical Formulation -- 10.5 Numerical Illustrations. , 10.6 Graphical Representations -- 10.7 Conclusion and Managerial Implications -- References -- 11 Software Engineering Analytics-The Need of Post COVID-19 Business: An Academic Review -- 11.1 Introduction -- 11.2 COVID19-Effects in Business -- 11.2.1 Tourism and Hospitality -- 11.2.2 Airlines and Aviation -- 11.2.3 Oil and Gas -- 11.2.4 Automotive Manufacturing -- 11.2.5 Consumer Products -- 11.2.6 Education -- 11.2.7 Software Industry -- 11.3 Research Methodology -- 11.4 Software Development Life Cycle (SDLC) -- 11.4.1 Software Requirements Analysis -- 11.4.2 Software Design -- 11.4.3 Software Implementation -- 11.4.4 Software Testing -- 11.4.5 Software Deployment -- 11.4.6 Software Maintenance -- 11.5 Software Analytics -- 11.5.1 Literature Analysis -- 11.5.2 Journal Publications with Frequency -- 11.5.3 Citations/Year -- 11.5.4 Software Engineering Analytics: Connotes -- 11.6 Conclusion -- References -- 12 The Rise and Fall of the SCOR Model: What After the Pandemic? -- 12.1 Introduction: The SCOR Model as a Supply Chain Management Foundation -- 12.2 The Evolution of Supply Chain Management -- 12.3 The SCOR Model-A Foundation for Supply Chain Management -- 12.3.1 Supply Chain Optimisation in the Digital Supply Chain Era -- 12.4 Is the Scor Model Still Relevant? -- 12.4.1 The Evolution of the Scor Model -- 12.4.2 The Fall of the Scor Model -- 12.4.3 Is the Resurrection of the Scor Model Possible? -- 12.5 Re-designing the Future of Supply Chains Using Scor -- 12.5.1 Re-designing for Supply Chain Reliability -- 12.5.2 Re-designing for Supply Chain Agility and Responsiveness -- 12.5.3 Re-designing for Supply Chain Asset Management -- 12.6 Concluding Remarks -- References -- Part II Management Optimization -- 13 A Comparative Study on Multi-objective Evolutionary Algorithms for Tri-objective Mean-Risk-Cardinality Portfolio Optimization Problems. , 13.1 Introduction -- 13.2 Portfolio Optimization -- 13.3 Multi-objective Evolutionary Algorithms -- 13.4 Multi-objective Evolutionary Algorithms for Portfolio Optimization Problems-A Literature Review -- 13.4.1 Mean-Variance Portfolio Optimization Using MOEAs -- 13.4.2 Mean-Risk Portfolio Optimization Using MOEAs -- 13.4.3 Three-Objective Portfolio Optimization Using MOEAs -- 13.5 Experimental Results -- 13.6 Conclusion -- References -- 14 Portfolio Insurance and Intelligent Algorithms -- 14.1 Introduction -- 14.2 Portfolio Insurance -- 14.2.1 Multi-period Portfolio Insurance Under Transaction Costs -- 14.3 Intelligent Algorithms -- 14.3.1 Beetle Antennae Search Algorithm -- 14.3.2 Popular Meta-Heuristic Algorithms -- 14.3.3 Multi-objective Optimization -- 14.3.4 The Main Algorithm for the MPMCPITC Problem -- 14.4 Portfolios' Applications with Real-World Data -- 14.4.1 Application in 4 Stocks' Portfolio -- 14.4.2 Application in 8 Stocks' Portfolio -- 14.4.3 Application in 12 Stocks' Portfolio -- 14.4.4 Application in 16 Stocks' Portfolio -- 14.4.5 Results and Performance Comparison of BAS, BA, FA and GA -- 14.5 Conclusion -- References -- 15 On Interval-Valued Multiobjective Programming Problems and Vector Variational-Like Inequalities Using Limiting Subdifferential -- 15.1 Introduction -- 15.2 Definitions and Preliminaries -- 15.3 Relationship Between (GMVVLI), (GSVVLI) and (IVMPP) -- 15.4 Relationship Between (WGMVVLI), (WGSVVLI) and (IVMPP) -- 15.5 Existence Results for (GMVVLI) and (GSVVLI) -- 15.6 Conclusions -- References -- 16 Portfolio Optimization Using Multi Criteria Decision Making -- 16.1 Introduction -- 16.2 An Overview of Multi Criteria Decision Making (MCDM) Process -- 16.3 Classification of Multi Criteria Decision Making Processes -- 16.4 Quantitative Techniques Applied for the MCDM -- 16.5 Financial Decision Problem. , 16.6 Risk Modeling and SWOT Analysis.