Multi-Criteria Decision Making for the Management of Complex Systems

Multi-Criteria Decision Making for the Management of Complex Systems

Albert Voronin (National Aviation University of Ukraine, Ukraine)
Release Date: March, 2017|Copyright: © 2017 |Pages: 201
ISBN13: 9781522525097|ISBN10: 1522525092|EISBN13: 9781522525103|DOI: 10.4018/978-1-5225-2509-7

Description

While there are many different models for performing system analysis, the multi-criteria decision making method has proven to be one of the most efficient. By analyzing the key concepts of this theory, the technique can be enhanced and will benefit future organizations and companies in novel ways.

Multi-Criteria Decision Making for the Management of Complex Systems provides a comprehensive examination of the latest strategies and methods involved in decision theory. Featuring extensive coverage on relevant topics such as nested scalar convolutions, Pareto optimality, nonlinear schemes, and operator performance, this publication is ideally designed for engineers, students, professionals, academics, and researchers seeking innovative perspectives on the supervision of advanced decision making theories in system analysis.

Topics Covered

The many academic areas covered in this publication include, but are not limited to:

  • Data Processing
  • E-Trainer
  • Hierarchical Structure Assessment
  • Nested Scalar Convolutions
  • Nonlinear Schemes
  • Operator Performance
  • Pareto Optimality
  • Perspective Space Facilities
  • Structural Optimization
  • Vector Quality Assessment

Table of Contents and List of Contributors

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Author(s)/Editor(s) Biography

Albert N. Voronin is a Doctor of Sciences (Eng.), Professor of Chair of Computer Information Technologies of National Aviation University of Ukraine; Member of American Mathematical Society. In 1957 he graduated from the Moscow Power Engineering Institute. His research interests include information technology of multicriteria decision-making, man-machine control systems, and synergistic aspects of mathematical statistics.