فهرس المقالات Pairwise comparison matrix

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  1 - Combined Data Envelopment Analysis and Analytical Hierarchy Process Methods to obtain the Favorable Weights from Pairwise Comparison Matrix
  Mehdi Fallah Jelodar
  Finding the favorable weights from pairwise comparison matrices is very important to rank the alternatives in Multi Attribute Decision Making (MADM) concept. Using Linear Programming has considered to develop this field during recent researches. Combined Analytic Hierar أکثر

  Finding the favorable weights from pairwise comparison matrices is very important to rank the alternatives in Multi Attribute Decision Making (MADM) concept. Using Linear Programming has considered to develop this field during recent researches. Combined Analytic Hierarchy Process and Data Envelopment Analysis considered by some researches to find the weights. Decreasing the numerical procedures is vital because of using linear programming in proposed models. In this paper we are going to present some models to generate the favorable weights from a pairwise comparison matrix combing Analytic Hierarchy Process (AHP) and Data Envelopment Analysis (DEA) models. First we address a history and applications of AHP and then some notes on DEA models. Finally three DEA models are proposed to find the weights. In this way we tried to decrease the computational process by decreasing the number of constraints and variables of proposed models. Some numerical examples are putted forward to illustrate our approaches. تفاصيل المقالة
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  2 - A goal programming procedure for ranking decision making units in DEA
  Farhad Hosseinzadeh-Lotfi Mohammad Izadikhah R. Roostaee Mohsen Rostamy-Malkhalifeh
  This research proposes a methodology for ranking decision making units byusing a goal programming model.We suggest a two phases procedure. In phase1, by using some DEA problems for each pair of units, we construct a pairwisecomparison matrix. Then this matrix is utilize أکثر

  This research proposes a methodology for ranking decision making units byusing a goal programming model.We suggest a two phases procedure. In phase1, by using some DEA problems for each pair of units, we construct a pairwisecomparison matrix. Then this matrix is utilized to rank the units via the goalprogramming model. تفاصيل المقالة
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  3 - A New Pythagorean Fuzzy Analytic Hierarchy Process Based on Interval-Valued Pythagorean Fuzzy Numbers
  Shahid Bhat Akanksha Singh Abdullah Qudaimi
  10.30495/fomj.2021.1940078.1037
  The Analytic Hierarchy Process (AHP) is one of the most widely used techniques to determine the priority weights of alternatives from pairwise comparison matrices. Several fuzzy and intuitionistic fuzzy extensions of AHP have been proposed in the literature. However, th أکثر

  The Analytic Hierarchy Process (AHP) is one of the most widely used techniques to determine the priority weights of alternatives from pairwise comparison matrices. Several fuzzy and intuitionistic fuzzy extensions of AHP have been proposed in the literature. However, these extensions are not appropriate to present some real-life situations. For this reason, several researchers extend the AHP to the Pythagorean Fuzzy Analytic Hierarchy Process (PFAHP). In the existing methods, an interval-valued Pythagorean fuzzy pairwise comparison matrix is transformed into a crisp matrix. Then crisp AHP is applied to obtain the normalized priority weights from the transformed crisp matrix. However, it is observed that the transformed crisp matrix, obtained on applying the step of existing methods, violates the reciprocal propriety of pairwise comparison matrices, and the obtained normalized priority weights are the weights of non-pairwise comparison matrices. Therefore, this paper discusses the shortcomings of the existing method, and a modified method is proposed to overcome these shortcomings. Finally, based on a real-life decision-making problem, the superiority of the proposed method over the existing method is shown. تفاصيل المقالة