Engineering analytics is becoming a necessary skill for every engineer. Areas such as Operations Research, Simulation, and Machine Learning can be totally transformed through massive volumes of data. This book is intended to be an introduction to Engineering Analytics that can be used to improve performance tracking, customer segmentation for resource optimization, patterns and classification strategies, and logistics control towers.
Basic methods in the areas of visual, descriptive, predictive, and prescriptive analytics and Big Data are introduced. Industrial case studies and example problem demonstrations are used throughout the book to reinforce the concepts and applications. The book goes on to cover visual analytics and its relationships, simulation from the respective dimensions and Machine Learning and Artificial Intelligence from different paradigms viewpoints.
The book is intended for professionals wanting to work on analytical problems, for Engineering students, Researchers, Chief-Technology Officers, and Directors that work within the areas and fields of Industrial Engineering, Computer Science, Statistics, Electrical Engineering Operations Research, and Big Data.
About the Author: Luis Rabelo, Ph.D., was the NASA EPSCoR Agency Project Manager and currently a Professor in the Department of Industrial Engineering and Management Systems at the University of Central Florida. He received dual degrees in Electrical and Mechanical Engineering from the Technological University of Panama and Master's degrees from the Florida Institute of Technology in Electrical Engineering (1987) and the University of Missouri-Rolla in Engineering Management (1988). He received a Ph.D. in Engineering Management from the University of Missouri-Rolla in 1990, where he also did Post-Doctoral work in Nuclear Engineering in 1990-1991. In addition, he holds a dual MS degree in Systems Engineering & Management from the Massachusetts Institute of Technology (MIT). He has over 280 publications, three international patents being utilized in the Aerospace Industry, and graduated 40 Master and 34 Doctoral students as advisor/Co-Advisor.
Edgar Gutierrez-Franco, is a Postdoctoral Associate at the Massachusetts Institute of Technology, MIT Center for Transportation and Logistics, CTL. He also serves since 2009 as a Research Affiliate at the Center for Latin America Logistics Innovation part of the MIT Global SCALE network and is Fulbright Scholar since 2014. Dr. Edgar has over twelve years of experience in data-driven decision-making software solutions to support business operations. His specialties are in Supply Chain Management, Applied Optimization and Machine Learning. He has experience in the consultancy, retail, and beverage industry and in academia in projects and training activities related with Operations Research and Supply Chain Management. His educational background includes a B.S. in Industrial Engineering from the University of La Sabana (2004), an MSc. in Industrial Engineering (Operations Research and Statistics) from the University of Los Andes (2008), and a Ph.D. in Industrial Engineering and Management Systems from the University of Central Florida (2019). During his time as a Visiting Scholar at the Center for Transportation and Logistics (2009-2010), he participated in projects of MIT CTL's corporate partners in Supply Chain Innovation in Emerging Markets and Carbon-Efficient Supply Chains.
Alfonso Sarmiento is an Associate Professor at the Program of Industrial Engineering, University of La Sabana, Colombia. He received his bachelor degree in Industrial Engineering from the University of Lima, Perú. He earned a M.S. degree from the Department of Industrial and Systems Engineering at the University of Florida. He obtained his PhD in Industrial Engineering with emphasis in Simulation Modeling from the University of Central Florida. Prior to working in the academia, Dr. Sarmiento had more than 10 years' experience as a consultant in operations process improvement. His current research focuses on supply chain stabilization methods, hybrid simulation and reinforcement learning.
Christopher Mejía-Argueta is a Research Scientist at the MIT Center for Transportation and Logistics. He is director and founder of the MIT Food and Retail Operations Lab (FaROL) where he develops applied research to improve the performance of supply chains. He is also director of the MIT SCALE network for Latin America and the Caribbean, as well as the MIT GCLOG program. He has more than 13 years of experience, he has solved logistical problems for more than 15 countries on four different continents. He is the editor and author of four books on supply chain management for emerging markets and has published dozens of industrial projects. He holds a PhD and a MSc from Monterrey Tech (Mex).
