Total area based on orthogonal vectors (Taov) as a novel method of multi-criteria decision aid

Seyed Hossein Razavi Hajiagha, Hannan Amoozad Mahdiraji, Shide Sadat Hashemi

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)
25 Downloads (Pure)


Multi criteria decision aid (MCDA) deals with the problem of evaluating a set of finite alternatives regard to a set of finite criteria. A remarkable volume of qualitative and quantitative researches are done on decision making methods and situations, indicating its important role for managers at different organizational levels. These types of problems are applied in many different fields of human life. A challenging feature of these problems is non-existence of an optimal solution due to considering multiple criteria and the proposed methods seeking to find a satisfactory solution called efficient of Pareto-optimal. In consideration of MCDA problem, in this paper a new method is proposed for solving DM problems, consisting three fundamental steps of initialization, orthogonalization, and comparison. Thus, a new MCDA method called total area based on orthogonal vectors (TAOV) is introduced. This method is constructed on orthogonality of decision criteria. Application of TAOV method is illustrated in a decision problem and its performance is evaluated regard to other MCDA methods. Furthermore, its features are explained around the features of a desirable MCDA method. The obtained results indicate that the TAOV method can be considered as an acceptable method of handling multi-criteria decision making problems.

Original languageEnglish
Pages (from-to)1679-1694
Number of pages16
JournalTechnological and Economic Development of Economy
Issue number4
Publication statusPublished - 1 Jan 2018
Externally publishedYes

Bibliographical note

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


  • Decision making
  • Multi-criteria decision aid
  • Orthogonality
  • Pareto-optimal
  • Principal component analysis
  • TAOV

ASJC Scopus subject areas

  • Finance


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