Decision-Making Management: A Tutorial and Applications provides practical guidance for researchers seeking to optimizing business-critical decisions employing Logical Decision Trees thus saving time and money. The book focuses on decision-making and resource allocation across and between the manufacturing, product design and logistical functions. It demonstrates key results for each sector with diverse real-world case studies drawn primarily from EU projects. Theory is accompanied by relevant analysis techniques, with a progressional approach building from simple theory to complex and dynamic decisions with multiple data points, including big data and lot of data. Binary Decision Diagrams are presented as the operating approach for evaluating large Logical Decision Trees, helping readers identify Boolean equations for quantitative analysis of multifaceted problem sets. Computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques are expertly blended to support analysis of multi-criteria decision-making problems with defined constraints and requirements. The final objective is to optimize dynamic decisions with original approaches employing useful tools, including Big Data analysis. Extensive annexes provide useful supplementary information for readers to follow methods contained in the book.

Explores the use of logical decision trees to solve business problems
Uses mathematical optimization techniques to resolve ‘big data’ or other multi-criteria problems
Provides annexes showcasing application in manufacturing, product design and logistics
Shows case examples in telecommunications, renewable energy and aerospace
Supplies introduction by Benjamin Lev, Editor-in-Chief of Omega, the highest-ranked journal in management science (JCR)

About the Authors

Dr. Alberto Pliego Marug�n holds a doctorate (cum laude) in Industrial Engineering at the University of Castilla-la Mancha (UCLM, Spain), with international mention. He is the main author of several works related to machine learning, optimization algorithms, maintenance management, and decision-making in industry. He worked at Everis and he is currently a PostDoc member of the Ingenium Research Group at UCLM.

Fausto Pedro Garc�a M�rquez works as a Professor and as Director of the Ingenium Research Group at the Universidad De Castilla-La Mancha, Spain. He is an Honorary Senior Research Fellow at Birmingham University, UK, and a Lecturer at the Postgraduate European Institute. He has published more than 150 papers and 31 books (Elsevier, Springer, Pearson, McGraw-Hill, Intech, IGI, Marcombo, AlfaOmega). He has been Principal Investigator in 4 European projects, 6 national projects, and more than 150 projects for universities, companies, and other institutions. His main interests are: Artificial Intelligence, Maintenance, Management, Renewable Energy, Transport, Advanced Analytics, and Data Science.

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