S. C. Malik, PhD, is a professor of Statistics at Maharshi Dayanand University Rohtak, India. He has published more than 170 research articles in international journals, has participated in about 80 national/international conferences and workshops, as well as authored 3 books. Deepak Sinwar, PhD, is an assistant professor in the Department of Computer and Communication Engineering, School of Computing & Information Technology at Manipal University Jaipur, Jaipur, Rajasthan, India. His research interests include computational intelligence, data mining, machine learning, reliability theory, computer networks, and pattern recognition. Ashish Kumar, PhD, is an assistant professor in the Department of Mathematics & Statistics, Manipal University Jaipur, Jaipur. He has published more than 80 research papers in various national/international journals and participated in more than 50 conferences in India and abroad. His area of interest is reliability modeling and analysis, sampling theory, reliability estimation, and data analysis. Gadde Srinivasa Rao, PhD, is a Professor of Statistics in the Department of Statistics, Dodoma University, Tanzania. He has published more than 140 articles in peer-reviewed journals and participated in more than 70 national and international conferences. His research interests include statistical inference, quality control, and reliability estimation. Prasenjit Chatterjee, PhD, is the Dean (Research and Consultancy) at MCKV Institute of Engineering, West Bengal, India. He has more than 100 research papers in various international journals and peer-reviewed conferences. He has authored and edited more than 20 books and is one of the developers of two multiple-criteria decision-making methods called Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) and Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval (RAFSI). Bui Thanh Hung, PhD, is the Director of the Artificial Intelligence Laboratory, Faculty of Information Technology, Ton Duc Thang University, Vietnam, and received his doctorate from Japan Advanced Institute of Science and Technology (JAIST) in 2013. He has published numerous research articles in international journals and conferences as well as 14 book chapters. His main research interests are natural language processing, machine learning, machine translation, text processing, data analytics, computer vision, and artificial intelligence.

• Preface xvAcknowledgment xxi1 Reliability Indices of a Computer System with Priority and Server Failure 1S.C. Malik, R.K. Yadav and N. Nandal1.1 Introduction 21.2 Some Fundamentals 41.2.1 Reliability 41.2.2 Mean Time to System Failure (MTSF) 41.2.3 Steady State Availability 41.2.4 Redundancy 51.2.5 Semi-Markov Process 51.2.6 Regenerative Point Process 61.3 Notations and Abbreviations 61.4 Assumptions and State Descriptions 81.5 Reliability Measures 91.5.1 Transition Probabilities 91.5.2 Mst 101.5.3 Reliability and MTCSF 101.5.4 Availability 111.5.5 Expected Number of Hardware Repairs 121.5.6 Expected Number of Software Upgradations 131.5.7 Expected Number of Treatments Given to the Server 141.5.8 Busy Period of Server Due to H/w Repair 151.5.9 Busy Period of Server Due to Software Upgradation 161.6 Profit Analysis 171.7 Particular Case 181.8 Graphical Presentation of Reliability Indices 191.9 Real-Life Application 201.10 Conclusion 21References 212 Mathematical Modeling and Availability Optimization of Turbine Using Genetic Algorithm 23Monika Saini, Nivedita Gupta and Ashish Kumar2.1 Introduction 232.2 System Description, Notations, and Assumptions 252.2.1 System Description 252.2.2 Notations 272.2.3 Assumptions 282.3 Mathematical Modeling of the System 282.4 Optimization 332.4.1 Genetic Algorithm 332.5 Results and Discussion 342.6 Conclusion 36References 453 Development of Laplacian Artificial Bee Colony Algorithm for Effective Harmonic Estimator Design 47Aishwarya Mehta, Jitesh Jangid, Akash Saxena, Shalini Shekhawat and Rajesh Kumar3.1 Introduction 483.2 Problem Formulation of Harmonics 523.3 Development of Laplacian Artificial Bee Colony Algorithm 543.3.1 Basic Concepts of ABC 543.3.2 The Proposed LABC Algorithm 563.4 Discussion 583.5 Numerical Validation of Proposed Variant 583.5.1 Comparative Analysis of LABC with Other Meta-Heuristics 593.5.2 Benchmark Test on CEC-17 Functions 703.6 Analytical Validation of Proposed Variant 723.6.1 Convergence Rate Test 753.6.2 Box Plot Analysis 773.6.3 Wilcoxon Rank Sum Test 773.6.4 Scalability Test 813.7 Design Analysis of Harmonic Estimator 813.7.1 Assessment of Harmonic Estimator Design Problem 1 813.7.2 Assessment of Harmonic Estimator Design Problem 2 873.8 Conclusion 92References 934 Applications of Cuckoo Search Algorithm in Reliability Optimization 97V. Kaviyarasu and V. Suganthi4.1 Introduction 984.2 Cuckoo Search Algorithm 984.2.1 Performance of Cuckoo Search Algorithm 984.2.2 Levy Flights 994.2.3 Software Reliability 994.3 Modified Cuckoo Search Algorithm (MCS) 1004.4 Optimization in Module Design 1024.5 Optimization at Dynamic Implementation 1034.6 Comparative Study of Support of Modified Cuckoo Search Algorithm 1044.7 Results and Discussions 1054.8 Conclusion 107References 1085 Series-Parallel Computer System Performance Evaluation with Human Operator Using Gumbel-Hougaard Family Copula 109Muhammad Salihu Isa, Ibrahim Yusuf, Uba Ahmad Ali and Wu Jinbiao5.1 Introduction 1105.2 Assumptions, Notations, and Description of the System 1125.2.1 Notations 1125.2.2 Assumptions 1145.2.3 Description of the System 1145.3 Reliability Formulation of Models 1165.3.1 Solution of the Model 1175.4 Some Particular Cases Based on Analytical Analysis of the Model 1205.4.1 Availability Analysis 1205.4.2 Reliability Analysis 1215.4.3 Mean Time to Failure (MTTF) 1225.4.4 Cost-Benefit Analysis 1245.5 Conclusions Through Result Discussion 125References 1266 Applications of Artificial Intelligence in Sustainable Energy Development and Utilization 129Aditya Kolakoti, Prasadarao Bobbili, Satyanarayana Katakam, Satish Geeri and Wasim Ghder Soliman6.1 Energy and Environment 1306.2 Sustainable Energy 1306.3 Artificial Intelligence in Industry 4.0 1316.4 Introduction to AI and its Working Mechanism 1326.5 Biodiesel 1356.6 Transesterification Process 1366.7 AI in Biodiesel Applications 1386.8 Conclusion 140References 1407 On New Joint Importance Measures for Multistate Reliability Systems 145Chacko V. M.7.1 Introduction 1457.2 New Joint Importance Measures 1477.2.1 Multistate Differential Joint Reliability Achievement Worth (MDJRAW) 1487.2.2 Multistate Differential Joint Reliability Reduction Worth (MDJRRW) 1507.2.3 Multistate Differential Joint Reliability Fussel-Vesely (MDJRFV) Measure 1527.3 Discussion 1537.4 Illustrative Example 1547.5 Conclusion 157References 1578 Inferences for Two Inverse Rayleigh Populations Based on Joint Progressively Type-II Censored Data 159Kapil Kumar and Anita Kumari8.1 Introduction 1598.2 Model Description 1618.3 Classical Estimation 1638.3.1 Maximum Likelihood Estimation 1638.3.2 Asymptotic Confidence Interval 1648.4 Bayesian Estimation 1668.4.1 Tierney-Kadane’s Approximation 1678.4.2 Metropolis-Hastings Algorithm 1698.4.3 HPD Credible Interval 1708.5 Simulation Study 1708.6 Real-Life Application 1768.7 Conclusions 177References 1779 Component Reliability Estimation Through Competing Risk Analysis of Fuzzy Lifetime Data 181Rashmi Bundel, M. S. Panwar and Sanjeev K. Tomer9.1 Introduction 1829.2 Fuzzy Lifetime Data 1839.2.1 Fuzzy Set 1839.2.2 Fuzzy Numbers and Membership Function 1849.2.3 Fuzzy Event and its Probability 1879.3 Modeling with Fuzzy Lifetime Data in Presence of Competing Risks 1879.4 Maximum Likelihood Estimation with Exponential Lifetimes 1899.4.1 Bootstrap Confidence Interval 1929.5 Bayes Estimation 1929.5.1 Highest Posterior Density Confidence Estimates 1949.6 Numerical Illustration 1959.6.1 Simulation Study 1969.6.2 Reliability Analysis Using Simulated Data 2109.7 Real Data Study 2129.8 Conclusion 212References 21510 Cost-Benefit Analysis of a Redundant System with Refreshment 217M.S. Barak and Dhiraj Yadav10.1 Introduction 21810.2 Notations 21910.3 Average Sojourn Times and Probabilities of Transition States 22010.4 Mean Time to Failure of the System 22310.5 Steady-State Availability 22310.6 The Period in Which the Server is Busy With Inspection 22410.7 Expected Number of Visits for Repair 22710.8 Expected Number of Refreshments 22710.9 Particular Case 22810. 10 Cost-Benefit Examination 23010.11 Discussion 23010.12 Conclusion 233References 23311 Fuzzy Information Inequalities, Triangular Discrimination and Applications in Multicriteria Decision Making 235Ram Naresh Saraswat and Sapna Gahlot11.1 Introduction 23511.2 New f-Divergence Measure on Fuzzy Sets 23711.3 New Fuzzy Information Inequalities Using Fuzzy New f-Divergence Measure and Fuzzy Triangular Divergence Measure 23911.4 Applications for Some Fuzzy f-Divergence Measures 24111.5 Applications in MCDM 24411.5.1 Case Study 24611.6 Conclusion 247References 24812 Contribution of Refreshment Provided to the Server During His Job in the Repairable Cold Standby System 251M.S. Barak, Ajay Kumar and Reena Garg12.1 Introduction 25212.2 The Assumptions and Notations Used to Solve the System 25412.3 The Probabilities of States Transitions 25612.4 Mean Sojourn Time 25712.5 Mean Time to Failure of the System 25712.6 Steady-State Availability 25812.7 Busy Period of the Server Due to Repair of the Failed Unit 25912.8 Busy Period of the Server Due to Refreshment 25912.9 Estimated Visits Made by the Server 26012.10 Particular Cases 26112.11 Profit Analysis 26212.12 Discussion 26212.13 Conclusion 26412.14 Contribution of Refreshment 26512.15 Future Scope 265References 26513 Stochastic Modeling and Availability Optimization of Heat Recovery Steam Generator Using Genetic Algorithm 269Monika Saini, Nivedita Gupta and Ashish Kumar13.1 Introduction 27013.2 System Description, Notations, and Assumptions 27113.2.1 System Description 27113.2.2 Notations 27213.2.3 Assumptions 27313.3 Mathematical Modeling of the System 27313.4 Availability Optimization of Proposed Model 27813.5 Results and Discussion 28013.6 Conclusion 285References 28514 Investigation of Reliability and Maintainability of Piston Manufacturing Plant 287Monika Saini, Deepak Sinwar and Ashish Kumar14.1 Introduction 28814.2 System Description and Data Collection 29014.3 Descriptive Analysis 29414.4 Power Law Process Model 29514.5 Trend and Serial Correlation Analysis 30014.6 Reliability and Maintainability Analysis 30214.7 Conclusion 306References 307Index 311

S. C. Malik, PhD, is a professor of Statistics at Maharshi Dayanand University Rohtak, India. He has published more than 170 research articles in international journals, has participated in about 80 national/international conferences and workshops, as well as authored 3 books. Deepak Sinwar, PhD, is an assistant professor in the Department of Computer and Communication Engineering, School of Computing & Information Technology at Manipal University Jaipur, Jaipur, Rajasthan, India. His research interests include computational intelligence, data mining, machine learning, reliability theory, computer networks, and pattern recognition. Ashish Kumar, PhD, is an assistant professor in the Department of Mathematics & Statistics, Manipal University Jaipur, Jaipur. He has published more than 80 research papers in various national/international journals and participated in more than 50 conferences in India and abroad. His area of interest is reliability modeling and analysis, sampling theory, reliability estimation, and data analysis. Gadde Srinivasa Rao, PhD, is a Professor of Statistics in the Department of Statistics, Dodoma University, Tanzania. He has published more than 140 articles in peer-reviewed journals and participated in more than 70 national and international conferences. His research interests include statistical inference, quality control, and reliability estimation. Prasenjit Chatterjee, PhD, is the Dean (Research and Consultancy) at MCKV Institute of Engineering, West Bengal, India. He has more than 100 research papers in various international journals and peer-reviewed conferences. He has authored and edited more than 20 books and is one of the developers of two multiple-criteria decision-making methods called Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) and Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval (RAFSI). Bui Thanh Hung, PhD, is the Director of the Artificial Intelligence Laboratory, Faculty of Information Technology, Ton Duc Thang University, Vietnam, and received his doctorate from Japan Advanced Institute of Science and Technology (JAIST) in 2013. He has published numerous research articles in international journals and conferences as well as 14 book chapters. His main research interests are natural language processing, machine learning, machine translation, text processing, data analytics, computer vision, and artificial intelligence.

• Preface xvAcknowledgment xxi1 Reliability Indices of a Computer System with Priority and Server Failure 1S.C. Malik, R.K. Yadav and N. Nandal1.1 Introduction 21.2 Some Fundamentals 41.2.1 Reliability 41.2.2 Mean Time to System Failure (MTSF) 41.2.3 Steady State Availability 41.2.4 Redundancy 51.2.5 Semi-Markov Process 51.2.6 Regenerative Point Process 61.3 Notations and Abbreviations 61.4 Assumptions and State Descriptions 81.5 Reliability Measures 91.5.1 Transition Probabilities 91.5.2 Mst 101.5.3 Reliability and MTCSF 101.5.4 Availability 111.5.5 Expected Number of Hardware Repairs 121.5.6 Expected Number of Software Upgradations 131.5.7 Expected Number of Treatments Given to the Server 141.5.8 Busy Period of Server Due to H/w Repair 151.5.9 Busy Period of Server Due to Software Upgradation 161.6 Profit Analysis 171.7 Particular Case 181.8 Graphical Presentation of Reliability Indices 191.9 Real-Life Application 201.10 Conclusion 21References 212 Mathematical Modeling and Availability Optimization of Turbine Using Genetic Algorithm 23Monika Saini, Nivedita Gupta and Ashish Kumar2.1 Introduction 232.2 System Description, Notations, and Assumptions 252.2.1 System Description 252.2.2 Notations 272.2.3 Assumptions 282.3 Mathematical Modeling of the System 282.4 Optimization 332.4.1 Genetic Algorithm 332.5 Results and Discussion 342.6 Conclusion 36References 453 Development of Laplacian Artificial Bee Colony Algorithm for Effective Harmonic Estimator Design 47Aishwarya Mehta, Jitesh Jangid, Akash Saxena, Shalini Shekhawat and Rajesh Kumar3.1 Introduction 483.2 Problem Formulation of Harmonics 523.3 Development of Laplacian Artificial Bee Colony Algorithm 543.3.1 Basic Concepts of ABC 543.3.2 The Proposed LABC Algorithm 563.4 Discussion 583.5 Numerical Validation of Proposed Variant 583.5.1 Comparative Analysis of LABC with Other Meta-Heuristics 593.5.2 Benchmark Test on CEC-17 Functions 703.6 Analytical Validation of Proposed Variant 723.6.1 Convergence Rate Test 753.6.2 Box Plot Analysis 773.6.3 Wilcoxon Rank Sum Test 773.6.4 Scalability Test 813.7 Design Analysis of Harmonic Estimator 813.7.1 Assessment of Harmonic Estimator Design Problem 1 813.7.2 Assessment of Harmonic Estimator Design Problem 2 873.8 Conclusion 92References 934 Applications of Cuckoo Search Algorithm in Reliability Optimization 97V. Kaviyarasu and V. Suganthi4.1 Introduction 984.2 Cuckoo Search Algorithm 984.2.1 Performance of Cuckoo Search Algorithm 984.2.2 Levy Flights 994.2.3 Software Reliability 994.3 Modified Cuckoo Search Algorithm (MCS) 1004.4 Optimization in Module Design 1024.5 Optimization at Dynamic Implementation 1034.6 Comparative Study of Support of Modified Cuckoo Search Algorithm 1044.7 Results and Discussions 1054.8 Conclusion 107References 1085 Series-Parallel Computer System Performance Evaluation with Human Operator Using Gumbel-Hougaard Family Copula 109Muhammad Salihu Isa, Ibrahim Yusuf, Uba Ahmad Ali and Wu Jinbiao5.1 Introduction 1105.2 Assumptions, Notations, and Description of the System 1125.2.1 Notations 1125.2.2 Assumptions 1145.2.3 Description of the System 1145.3 Reliability Formulation of Models 1165.3.1 Solution of the Model 1175.4 Some Particular Cases Based on Analytical Analysis of the Model 1205.4.1 Availability Analysis 1205.4.2 Reliability Analysis 1215.4.3 Mean Time to Failure (MTTF) 1225.4.4 Cost-Benefit Analysis 1245.5 Conclusions Through Result Discussion 125References 1266 Applications of Artificial Intelligence in Sustainable Energy Development and Utilization 129Aditya Kolakoti, Prasadarao Bobbili, Satyanarayana Katakam, Satish Geeri and Wasim Ghder Soliman6.1 Energy and Environment 1306.2 Sustainable Energy 1306.3 Artificial Intelligence in Industry 4.0 1316.4 Introduction to AI and its Working Mechanism 1326.5 Biodiesel 1356.6 Transesterification Process 1366.7 AI in Biodiesel Applications 1386.8 Conclusion 140References 1407 On New Joint Importance Measures for Multistate Reliability Systems 145Chacko V. M.7.1 Introduction 1457.2 New Joint Importance Measures 1477.2.1 Multistate Differential Joint Reliability Achievement Worth (MDJRAW) 1487.2.2 Multistate Differential Joint Reliability Reduction Worth (MDJRRW) 1507.2.3 Multistate Differential Joint Reliability Fussel-Vesely (MDJRFV) Measure 1527.3 Discussion 1537.4 Illustrative Example 1547.5 Conclusion 157References 1578 Inferences for Two Inverse Rayleigh Populations Based on Joint Progressively Type-II Censored Data 159Kapil Kumar and Anita Kumari8.1 Introduction 1598.2 Model Description 1618.3 Classical Estimation 1638.3.1 Maximum Likelihood Estimation 1638.3.2 Asymptotic Confidence Interval 1648.4 Bayesian Estimation 1668.4.1 Tierney-Kadane’s Approximation 1678.4.2 Metropolis-Hastings Algorithm 1698.4.3 HPD Credible Interval 1708.5 Simulation Study 1708.6 Real-Life Application 1768.7 Conclusions 177References 1779 Component Reliability Estimation Through Competing Risk Analysis of Fuzzy Lifetime Data 181Rashmi Bundel, M. S. Panwar and Sanjeev K. Tomer9.1 Introduction 1829.2 Fuzzy Lifetime Data 1839.2.1 Fuzzy Set 1839.2.2 Fuzzy Numbers and Membership Function 1849.2.3 Fuzzy Event and its Probability 1879.3 Modeling with Fuzzy Lifetime Data in Presence of Competing Risks 1879.4 Maximum Likelihood Estimation with Exponential Lifetimes 1899.4.1 Bootstrap Confidence Interval 1929.5 Bayes Estimation 1929.5.1 Highest Posterior Density Confidence Estimates 1949.6 Numerical Illustration 1959.6.1 Simulation Study 1969.6.2 Reliability Analysis Using Simulated Data 2109.7 Real Data Study 2129.8 Conclusion 212References 21510 Cost-Benefit Analysis of a Redundant System with Refreshment 217M.S. Barak and Dhiraj Yadav10.1 Introduction 21810.2 Notations 21910.3 Average Sojourn Times and Probabilities of Transition States 22010.4 Mean Time to Failure of the System 22310.5 Steady-State Availability 22310.6 The Period in Which the Server is Busy With Inspection 22410.7 Expected Number of Visits for Repair 22710.8 Expected Number of Refreshments 22710.9 Particular Case 22810. 10 Cost-Benefit Examination 23010.11 Discussion 23010.12 Conclusion 233References 23311 Fuzzy Information Inequalities, Triangular Discrimination and Applications in Multicriteria Decision Making 235Ram Naresh Saraswat and Sapna Gahlot11.1 Introduction 23511.2 New f-Divergence Measure on Fuzzy Sets 23711.3 New Fuzzy Information Inequalities Using Fuzzy New f-Divergence Measure and Fuzzy Triangular Divergence Measure 23911.4 Applications for Some Fuzzy f-Divergence Measures 24111.5 Applications in MCDM 24411.5.1 Case Study 24611.6 Conclusion 247References 24812 Contribution of Refreshment Provided to the Server During His Job in the Repairable Cold Standby System 251M.S. Barak, Ajay Kumar and Reena Garg12.1 Introduction 25212.2 The Assumptions and Notations Used to Solve the System 25412.3 The Probabilities of States Transitions 25612.4 Mean Sojourn Time 25712.5 Mean Time to Failure of the System 25712.6 Steady-State Availability 25812.7 Busy Period of the Server Due to Repair of the Failed Unit 25912.8 Busy Period of the Server Due to Refreshment 25912.9 Estimated Visits Made by the Server 26012.10 Particular Cases 26112.11 Profit Analysis 26212.12 Discussion 26212.13 Conclusion 26412.14 Contribution of Refreshment 26512.15 Future Scope 265References 26513 Stochastic Modeling and Availability Optimization of Heat Recovery Steam Generator Using Genetic Algorithm 269Monika Saini, Nivedita Gupta and Ashish Kumar13.1 Introduction 27013.2 System Description, Notations, and Assumptions 27113.2.1 System Description 27113.2.2 Notations 27213.2.3 Assumptions 27313.3 Mathematical Modeling of the System 27313.4 Availability Optimization of Proposed Model 27813.5 Results and Discussion 28013.6 Conclusion 285References 28514 Investigation of Reliability and Maintainability of Piston Manufacturing Plant 287Monika Saini, Deepak Sinwar and Ashish Kumar14.1 Introduction 28814.2 System Description and Data Collection 29014.3 Descriptive Analysis 29414.4 Power Law Process Model 29514.5 Trend and Serial Correlation Analysis 30014.6 Reliability and Maintainability Analysis 30214.7 Conclusion 306References 307Index 311

S. C. Malik, PhD, is a professor of Statistics at Maharshi Dayanand University Rohtak, India. He has published more than 170 research articles in international journals, has participated in about 80 national/international conferences and workshops, as well as authored 3 books. Deepak Sinwar, PhD, is an assistant professor in the Department of Computer and Communication Engineering, School of Computing & Information Technology at Manipal University Jaipur, Jaipur, Rajasthan, India. His research interests include computational intelligence, data mining, machine learning, reliability theory, computer networks, and pattern recognition. Ashish Kumar, PhD, is an assistant professor in the Department of Mathematics & Statistics, Manipal University Jaipur, Jaipur. He has published more than 80 research papers in various national/international journals and participated in more than 50 conferences in India and abroad. His area of interest is reliability modeling and analysis, sampling theory, reliability estimation, and data analysis. Gadde Srinivasa Rao, PhD, is a Professor of Statistics in the Department of Statistics, Dodoma University, Tanzania. He has published more than 140 articles in peer-reviewed journals and participated in more than 70 national and international conferences. His research interests include statistical inference, quality control, and reliability estimation. Prasenjit Chatterjee, PhD, is the Dean (Research and Consultancy) at MCKV Institute of Engineering, West Bengal, India. He has more than 100 research papers in various international journals and peer-reviewed conferences. He has authored and edited more than 20 books and is one of the developers of two multiple-criteria decision-making methods called Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) and Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval (RAFSI). Bui Thanh Hung, PhD, is the Director of the Artificial Intelligence Laboratory, Faculty of Information Technology, Ton Duc Thang University, Vietnam, and received his doctorate from Japan Advanced Institute of Science and Technology (JAIST) in 2013. He has published numerous research articles in international journals and conferences as well as 14 book chapters. His main research interests are natural language processing, machine learning, machine translation, text processing, data analytics, computer vision, and artificial intelligence.

• Preface xvAcknowledgment xxi1 Reliability Indices of a Computer System with Priority and Server Failure 1S.C. Malik, R.K. Yadav and N. Nandal1.1 Introduction 21.2 Some Fundamentals 41.2.1 Reliability 41.2.2 Mean Time to System Failure (MTSF) 41.2.3 Steady State Availability 41.2.4 Redundancy 51.2.5 Semi-Markov Process 51.2.6 Regenerative Point Process 61.3 Notations and Abbreviations 61.4 Assumptions and State Descriptions 81.5 Reliability Measures 91.5.1 Transition Probabilities 91.5.2 Mst 101.5.3 Reliability and MTCSF 101.5.4 Availability 111.5.5 Expected Number of Hardware Repairs 121.5.6 Expected Number of Software Upgradations 131.5.7 Expected Number of Treatments Given to the Server 141.5.8 Busy Period of Server Due to H/w Repair 151.5.9 Busy Period of Server Due to Software Upgradation 161.6 Profit Analysis 171.7 Particular Case 181.8 Graphical Presentation of Reliability Indices 191.9 Real-Life Application 201.10 Conclusion 21References 212 Mathematical Modeling and Availability Optimization of Turbine Using Genetic Algorithm 23Monika Saini, Nivedita Gupta and Ashish Kumar2.1 Introduction 232.2 System Description, Notations, and Assumptions 252.2.1 System Description 252.2.2 Notations 272.2.3 Assumptions 282.3 Mathematical Modeling of the System 282.4 Optimization 332.4.1 Genetic Algorithm 332.5 Results and Discussion 342.6 Conclusion 36References 453 Development of Laplacian Artificial Bee Colony Algorithm for Effective Harmonic Estimator Design 47Aishwarya Mehta, Jitesh Jangid, Akash Saxena, Shalini Shekhawat and Rajesh Kumar3.1 Introduction 483.2 Problem Formulation of Harmonics 523.3 Development of Laplacian Artificial Bee Colony Algorithm 543.3.1 Basic Concepts of ABC 543.3.2 The Proposed LABC Algorithm 563.4 Discussion 583.5 Numerical Validation of Proposed Variant 583.5.1 Comparative Analysis of LABC with Other Meta-Heuristics 593.5.2 Benchmark Test on CEC-17 Functions 703.6 Analytical Validation of Proposed Variant 723.6.1 Convergence Rate Test 753.6.2 Box Plot Analysis 773.6.3 Wilcoxon Rank Sum Test 773.6.4 Scalability Test 813.7 Design Analysis of Harmonic Estimator 813.7.1 Assessment of Harmonic Estimator Design Problem 1 813.7.2 Assessment of Harmonic Estimator Design Problem 2 873.8 Conclusion 92References 934 Applications of Cuckoo Search Algorithm in Reliability Optimization 97V. Kaviyarasu and V. Suganthi4.1 Introduction 984.2 Cuckoo Search Algorithm 984.2.1 Performance of Cuckoo Search Algorithm 984.2.2 Levy Flights 994.2.3 Software Reliability 994.3 Modified Cuckoo Search Algorithm (MCS) 1004.4 Optimization in Module Design 1024.5 Optimization at Dynamic Implementation 1034.6 Comparative Study of Support of Modified Cuckoo Search Algorithm 1044.7 Results and Discussions 1054.8 Conclusion 107References 1085 Series-Parallel Computer System Performance Evaluation with Human Operator Using Gumbel-Hougaard Family Copula 109Muhammad Salihu Isa, Ibrahim Yusuf, Uba Ahmad Ali and Wu Jinbiao5.1 Introduction 1105.2 Assumptions, Notations, and Description of the System 1125.2.1 Notations 1125.2.2 Assumptions 1145.2.3 Description of the System 1145.3 Reliability Formulation of Models 1165.3.1 Solution of the Model 1175.4 Some Particular Cases Based on Analytical Analysis of the Model 1205.4.1 Availability Analysis 1205.4.2 Reliability Analysis 1215.4.3 Mean Time to Failure (MTTF) 1225.4.4 Cost-Benefit Analysis 1245.5 Conclusions Through Result Discussion 125References 1266 Applications of Artificial Intelligence in Sustainable Energy Development and Utilization 129Aditya Kolakoti, Prasadarao Bobbili, Satyanarayana Katakam, Satish Geeri and Wasim Ghder Soliman6.1 Energy and Environment 1306.2 Sustainable Energy 1306.3 Artificial Intelligence in Industry 4.0 1316.4 Introduction to AI and its Working Mechanism 1326.5 Biodiesel 1356.6 Transesterification Process 1366.7 AI in Biodiesel Applications 1386.8 Conclusion 140References 1407 On New Joint Importance Measures for Multistate Reliability Systems 145Chacko V. M.7.1 Introduction 1457.2 New Joint Importance Measures 1477.2.1 Multistate Differential Joint Reliability Achievement Worth (MDJRAW) 1487.2.2 Multistate Differential Joint Reliability Reduction Worth (MDJRRW) 1507.2.3 Multistate Differential Joint Reliability Fussel-Vesely (MDJRFV) Measure 1527.3 Discussion 1537.4 Illustrative Example 1547.5 Conclusion 157References 1578 Inferences for Two Inverse Rayleigh Populations Based on Joint Progressively Type-II Censored Data 159Kapil Kumar and Anita Kumari8.1 Introduction 1598.2 Model Description 1618.3 Classical Estimation 1638.3.1 Maximum Likelihood Estimation 1638.3.2 Asymptotic Confidence Interval 1648.4 Bayesian Estimation 1668.4.1 Tierney-Kadane’s Approximation 1678.4.2 Metropolis-Hastings Algorithm 1698.4.3 HPD Credible Interval 1708.5 Simulation Study 1708.6 Real-Life Application 1768.7 Conclusions 177References 1779 Component Reliability Estimation Through Competing Risk Analysis of Fuzzy Lifetime Data 181Rashmi Bundel, M. S. Panwar and Sanjeev K. Tomer9.1 Introduction 1829.2 Fuzzy Lifetime Data 1839.2.1 Fuzzy Set 1839.2.2 Fuzzy Numbers and Membership Function 1849.2.3 Fuzzy Event and its Probability 1879.3 Modeling with Fuzzy Lifetime Data in Presence of Competing Risks 1879.4 Maximum Likelihood Estimation with Exponential Lifetimes 1899.4.1 Bootstrap Confidence Interval 1929.5 Bayes Estimation 1929.5.1 Highest Posterior Density Confidence Estimates 1949.6 Numerical Illustration 1959.6.1 Simulation Study 1969.6.2 Reliability Analysis Using Simulated Data 2109.7 Real Data Study 2129.8 Conclusion 212References 21510 Cost-Benefit Analysis of a Redundant System with Refreshment 217M.S. Barak and Dhiraj Yadav10.1 Introduction 21810.2 Notations 21910.3 Average Sojourn Times and Probabilities of Transition States 22010.4 Mean Time to Failure of the System 22310.5 Steady-State Availability 22310.6 The Period in Which the Server is Busy With Inspection 22410.7 Expected Number of Visits for Repair 22710.8 Expected Number of Refreshments 22710.9 Particular Case 22810. 10 Cost-Benefit Examination 23010.11 Discussion 23010.12 Conclusion 233References 23311 Fuzzy Information Inequalities, Triangular Discrimination and Applications in Multicriteria Decision Making 235Ram Naresh Saraswat and Sapna Gahlot11.1 Introduction 23511.2 New f-Divergence Measure on Fuzzy Sets 23711.3 New Fuzzy Information Inequalities Using Fuzzy New f-Divergence Measure and Fuzzy Triangular Divergence Measure 23911.4 Applications for Some Fuzzy f-Divergence Measures 24111.5 Applications in MCDM 24411.5.1 Case Study 24611.6 Conclusion 247References 24812 Contribution of Refreshment Provided to the Server During His Job in the Repairable Cold Standby System 251M.S. Barak, Ajay Kumar and Reena Garg12.1 Introduction 25212.2 The Assumptions and Notations Used to Solve the System 25412.3 The Probabilities of States Transitions 25612.4 Mean Sojourn Time 25712.5 Mean Time to Failure of the System 25712.6 Steady-State Availability 25812.7 Busy Period of the Server Due to Repair of the Failed Unit 25912.8 Busy Period of the Server Due to Refreshment 25912.9 Estimated Visits Made by the Server 26012.10 Particular Cases 26112.11 Profit Analysis 26212.12 Discussion 26212.13 Conclusion 26412.14 Contribution of Refreshment 26512.15 Future Scope 265References 26513 Stochastic Modeling and Availability Optimization of Heat Recovery Steam Generator Using Genetic Algorithm 269Monika Saini, Nivedita Gupta and Ashish Kumar13.1 Introduction 27013.2 System Description, Notations, and Assumptions 27113.2.1 System Description 27113.2.2 Notations 27213.2.3 Assumptions 27313.3 Mathematical Modeling of the System 27313.4 Availability Optimization of Proposed Model 27813.5 Results and Discussion 28013.6 Conclusion 285References 28514 Investigation of Reliability and Maintainability of Piston Manufacturing Plant 287Monika Saini, Deepak Sinwar and Ashish Kumar14.1 Introduction 28814.2 System Description and Data Collection 29014.3 Descriptive Analysis 29414.4 Power Law Process Model 29514.5 Trend and Serial Correlation Analysis 30014.6 Reliability and Maintainability Analysis 30214.7 Conclusion 306References 307Index 311