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Multi-Criteria Decision Making (MCDM) Using Artificial Intelligence (AI)

Special Issue Information

Dear Colleagues,

Organizations face problems during the process of complex decision making in multifaceted situations where multiple criteria and factors are involved. Real-world decision-support systems require consideration and analysis on the basis of multiple criteria which, in turn, affect the final decisions. Researchers concerned with the design and development of intelligent decision-making systems hunt for innovative scientific techniques, tools and models to improve the quality of the anticipated decisions. 

To achieve this goal of improved decision making, multi-criteria decision making (MCDM) and artificial intelligence (AI) techniques have recently been extensively practiced by researchers. As a result, significant improvements have been observed in decisions for a wide range of real-world complex problems. The integration of MCDM and AI offers new competencies to the configuration of complex decision making in different environments (e.g., static and distributed). These comprise the management of large datasets, the construction and modelling of innovative decision models, and the development of effective computational optimization algorithms for problem solving.

This Special Issue aims to solicit high-quality original research and review articles that cover novel, cutting-edge technologies and methods concerned with the scientific design, development and implementation of decision-support systems on the basis of MCDM and AI.

Potential topics include:

  • Intelligent decision-support technologies;
  • Data mining models for decision making;
  • Evidential reasoning;
  • Machine learning and deep learning models for decision making;
  • Evolutionary multiobjective optimization;
  • Fuzzy modelling;
  • Computational intelligence in MDCM;
  • Multi-criteria models for intelligent decision support system (IDSS) assessment;
  • Multi-attribute decision support;
  • Rule-based approach to multicriteria decision making;
  • Application of evidence theory in MCDM;
  • Multiobjective optimization problems;
  • Fuzzy multiobjective optimization;
  • Applications of the mentioned techniques across a wide range of areas, including, business, healthcare, education, industry, research, management, engineering, etc.

Dr. Rahman Ali
Dr. Asad Masood Khattak
Dr. Farkhund Iqbal
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • multi-criteria decision making
  • intelligent decision making
  • intelligent decision-support systems
  • machine learning
  • data mining
  • deep learning
Graphical abstract

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Appl. Sci. - ISSN 2076-3417Creative Common CC BY license