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.

Key Features

  • 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)


Graduate students and professionals in business administration, industrial organization, operations management, applied microeconomics, and the decisions sciences, either studying decision-making analysis, or who are required to solve large, specific and complex multi-criteria decision-making problems as part of their jobs. The work will also be of interest to industrial engineers and engineering designers working with optimization problems, but this is not the main audience

Table of Contents

1. Introduction.

2. Logical Decision Tree Analysis.

3. Binary Decision Diagrams.

4. Case Studies.

5. LDT: Dynamic Analysis.

6. Decision-Making Optimization 


No. of pages:
© Academic Press 2017
21st July 2017
Academic Press
eBook ISBN:
Paperback ISBN:

About the Authors

Alberto Pliego Marugan

Dr. Alberto Pliego Marugán 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 Garcia Marquez

Dr. Fausto Pedro García Márquez is Senior Lecturer (with Tenure and Full Professor Accredited from 2013) at UCLM (Spain), Honorary Senior Research Fellow at Bimingham University (UK), and recently he was a Senior Manager at Accenture. He is director of Ingenium Research Group, author of more than 150 papers, 19 books and 5 patents in Business Management. He had been awarded with more than 10 international prizes.


Abaut this book

This book focuses on the analytic principles of business practice and big data. Specifically, it provides an interface between the main disciplines of engineering/technology and the organizational and administrative aspects of management, serving as a complement to books in other disciplines such as economics, finance, marketing and risk analysis. The contributors present their areas of expertise, together with essential case studies that illustrate the successful application of engineering management theories in real-life examples.


Dr. Fausto Pedro García Márquez completed his European Doctorate in Engineering at the University of Castilla-La Mancha (UCLM) in 2004. He received his Engineering degree from the University of Murcia, Spain in 1998, and his Technical Engineering degree at UCLM in 1995 and degree in Business Administration and Management at UCLM in 2006. He has also served as Technician in Labor Risk Prevention by UCLM (2000) and Transport Specialist at the Polytechnic University of Madrid, Spain (2001). He was a Senior Manager at Accenture in 2013/2014, and is currently a Senior Lecturer (Full Professor accredited) at UCLM, an Honorary Senior Research Fellow at the University of Birmingham, UK, a Lecturer at the Instituto Europeo de Postgrado and Director of the Ingenium Research Group. He has been the principal investigator in 3 European Projects and 60 national and corporate research projects. He holds international and national patents, and has authored more than 110 international papers and 10 books. His work has been recognized with 3 International Awards in Engineering Management and Management Science. 

Dr. Benjamin Lev is a Professor and Head of Decision Sciences at LeBow College of Business. He holds a PhD in Operations Research from Case Western Reserve University. Prior to joining Drexel University, Dr. Lev held academic and administrative positions at Temple University, the University of Michigan-Dearborn and Worcester Polytechnic Institute. He is the Editor-in-Chief of OMEGA – The International journal of Management Science, the Co-Editor-in-Chief of the International Journal of Management Science and Engineering Management, and serves on several other journal editorial boards. He has published over ten books and numerous articles, and has organized many national and international conferences.

Table of contents (11 chapters)

  • Visualizing Big Data: Everything Old Is New Again. Chiera, Belinda A. (et al.) Pages 1-27

  • Managing Cloud-Based Big Data Platforms: A Reference Architecture and Cost Perspective. Heilig, Leonard (et al.) Pages 29-45

  • The Strategic Business Value of Big Data. Serrato, Marco (et al.) Pages 47-70

  • A Review on Big Data Security and Privacy in Healthcare Applications. Aqeel-ur-Rehman (et al.). Pages 71-89

  • What Is Big Data. Kinoshita, Eizo (et al.). Pages 91-101

  • Big Data for Conversational Interfaces: Current Opportunities and Prospects. Griol, David (et al.). Pages 103-121

  • Big Data Analytics in Telemedicine: A Role of Medical Image Compression. Bairagi, Vinayak K. Pages 123-160

  • A Bundle-Like Algorithm for Big Data Network Design with Risk-Averse Signal Control Optimization. Chiou, Suh-Wen. Pages 161-199

  • Evaluation of Evacuation Corridors and Traffic Big Data Management Strategies for Short-Notice Evacuation. Bu, Lei (et al.). Pages 201-225

  • Analyzing Network Log Files Using Big Data Techniques. Plaza-Martín, Víctor (et al.). Pages 227-256

  • Big Data and Earned Value Management in Airspace Industry. Rodríguez, Juan Carlos Meléndez (et al.). Pages 257-267


Del 28 de Julio al 2 de Agosto de 2017, se ha celebrado en Kanazawa (Japón) una nueva edición de la "International Conference on Management Science and Engineering Management (ICSEM)", organizada por la "International Society of Management Science and Engineering Management" y co-organizada por la Universidad de Kanazawa y Sichuan. Los miembros del grupo Ingenium Research Group, Fausto Pedro García, Alberto Pliego y Alfredo Arcos, junto a Jesús María Pinar, del grupo CUNEF-Ingenium, han presentado sus actuales trabajos de investigación publicados en el libro "Proceedings of the Eleventh International Conference on Managmenet Science and Engineering Management".

Ésta conferencia ha ofrecido una gran oportunidad a los investigadores para intercambiar ideas, resultados y experiencias sobre sus investigaciones y afrontar los retos científicos del futuro.