Predictive analytics refers to making predictions about the future based on different parameters which are historical data, machine learning, and artificial intelligence. This book provides the most recent advances in the field along with case studies and real-world examples.
It discusses predictive modeling and analytics in reliability engineering and introduces current achievements and applications of artificial intelligence, data mining, and other techniques in supply chain management. It covers applications to reliability engineering practice, presents numerous examples to illustrate the theoretical results, and considers and analyses case studies and real-word examples.
The book is written for researchers and practitioners in the field of system reliability, quality, supply chain management, and logistics management. Students taking courses in these areas will also find this book of interest.
Chapter 1 Role of MCDM in Software Reliability Engineering
Chapter 2
Fault Tree Analysis of a Computerized Numerical Control Turning Center
Chapter 3 How to Schedule Elective Patients in Hospitals to Gain Full
Utilization of Resources and Eliminate Patient Overcrowding
Chapter 4
Reducing the Deterioration Rate of Inventory through Preservation Technology
Investment under Fuzzy and Cloud Fuzzy Environment
Chapter 5 Image Formation
Using Deep Convolutional Generative Adversarial Networks
Chapter 6 Optimal
Preservation Technology Investment and Price for the Deteriorating Inventory
Model with Price-Sensitivity Stock- Dependent Demand
Chapter 7 EOQ with
Shortages and Learning Effect
Chapter 8 Optimal Production-Inventory Policies
for Processed Fruit Juices Manufacturer and Multi-retailers with Trended
Demand and Quality Degradation
Chapter 9 Information Visualization:
Perception and Limitations for Data-Driven Designs
Chapter 10 IoT, Big Data,
and Analytics Challenges and Opportunities
Chapter 11 Multiple-Criteria
Decision Analysis Using VLSI Global Routing
Chapter 12 Application of IoT in
Water Supply Management
Chapter 13 A Hybrid Approach for Video Indexing Using
Computer Vision and Speech Recognition
Chapter 14 Statistical Methodology for
Software Reliability with Environmental Factors
Chapter 15 Maintenance
Data-Trends Based Reliability Availability and Maintainability (RAM)
Assessment of a Steam Boiler
Dr. Vijay Kumar received his M.Sc. in Applied Mathematics and M.Phil. in Mathematics from Indian Institute of Technology (IIT), Roorkee, India in 1998 and 2000, respectively. He has completed his PhD from the Department of Operational Research, University of Delhi. Currently, He has published more than 40 research papers in the areas of software reliability, mathematical modeling and optimization in international journals and conferences of high repute. His current research interests include software reliability growth modelling, optimal control theory and marketing models in the context of innovation diffusion theory. He has reviewed many papers for IEEE Trans. on Reliability, Soft Computing (Springer), IJRQSE, IJQRM, IJSAEM and other reputed journals. He has edited special issues of IJAMS and RIO journal. He is an editorial board member of IJMEMS. He is a life member of Society for Reliability Engineering, Quality and Operations Management (SREQOM) and IEEE.
Dr. Mangey Ram received his Ph.D., major with Mathematics and minor in Computer Science from G. B. Pant University of Agriculture and Technology, Pantnagar in 2008. He is an editorial board member in many international journals. He has published 130 research publications in national and international journals of repute. His fields of research are Operations Research, Reliability Theory, Fuzzy Reliability, and System Engineering. Currently, he is working as a Professor at Graphic Era University.
