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Pairwise comparison

About: Pairwise comparison is a research topic. Over the lifetime, 6804 publications have been published within this topic receiving 174081 citations.


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Journal ArticleDOI
03 Mar 2019
TL;DR: A hybrid method which combined the Full Consistency Method (FUCOM) and Analytical Hierarchy Process (AHP) in one system has been used to assess the four Libyan airlines and shows that the reliability is the most important performance area followed by satisfaction.
Abstract: Performance measurement and evaluation of the airlines are a key point for improving their performance. This evaluation can help achieving the airline targets. The aim of this paper is to evaluate and compare the performance of four Libyan airlines by considering five main areas of performance; the airline reliability, employees, management, customer's satisfaction and tangibles. In this work, a hybrid method which combined the Full Consistency Method (FUCOM) and Analytical Hierarchy Process (AHP) in one system has been used to assess the four Libyan airlines. In the AHP method, the number of the required pairwise comparisons are increases dramatically with the number of the elements to be compared. The more the comparisons are the higher is the likelihood that the decision maker will introduce erroneous data. In this regard, the problem has been solved by means of using integer, decimal values from the predefined scale for the pairwise comparison of the criteria. The results show that the reliability is the most important performance area followed by satisfaction. Among the four investigated airlines, Libyan Wings were ranked first with a total 0.392 score.

50 citations

Journal ArticleDOI
01 Jun 2015
TL;DR: This paper upgrades the algorithm initially proposed for obtaining the PCCM in two ways: (i) it considers the case of different weights for the decision makers; and (ii) it strengthens the idea of consistency in the design of the algorithm.
Abstract: The Precise Consensus Consistency Matrix (PCCM) is an AHP-Group Decision Making (AHP-GDM) tool, defined by Aguaron et al. 2] and developed in a local context (a single criterion) in which the decision makers are assigned the same weights. Using the Row Geometric Mean as the prioritisation procedure, consensus is sought between the different decision makers when the modifications of their initial positions or judgements are guaranteed to be within the range of values accepted for a given inconsistency level. This paper upgrades the algorithm initially proposed for obtaining the PCCM in two ways: (i) it considers the case of different weights for the decision makers; and (ii) it strengthens the idea of consistency in the design of the algorithm. One of the drawbacks of this decisional tool is that it is sometimes impossible to achieve a complete matrix. To address this, we propose a procedure for attaining a complete common consensus judgement matrix or, at least, a matrix with the minimum number of entries that are required to derive the priorities. Finally, we compare the results obtained when applying the extensions of the PCCM with those obtained using the two traditional procedures (AIJ and AIP) usually employed in AHP-GDM. In order to do this, we use a set of indicators that measure the violations in consistency of the group pairwise matrices and the compatibility between the individuals and group positions in four cases associated with two scenarios (weighted and non-weighted decision makers) and two situations (complete and incomplete PCCMs). New method for consensus building in AHP-group decision makingMinor changes from individual pairwise comparison matricesChanges are consistent with the individual positions.Several measures to compare group consensus building methodsConsistency and compatibility of complete and incomplete group consensus matrices

50 citations

Journal ArticleDOI
TL;DR: The Gaussian Graphical Model (GGM) as discussed by the authors is a powerful and intuitive way to analyze dependencies in multivariate data, which is a key assumption of the GGM is that each pairwise i...
Abstract: Pairwise network models such as the Gaussian Graphical Model (GGM) are a powerful and intuitive way to analyze dependencies in multivariate data. A key assumption of the GGM is that each pairwise i...

50 citations

Journal ArticleDOI
01 Oct 2020
TL;DR: An integrated multi-criteria decision making (MCDM) framework wherein the weights of the criteria based on experts’ opinions are derived using PIvot Pairwise RElative Criteria Importance Assessment (PIPRECIA) method is presented.
Abstract: TThe supply chain forms the backbone of any organization. However, the effectiveness and efficiency of every activity get manifested in the financial outcome. Hence, measuring supply chain performance using financial metrics carries significance. The purpose of this paper is to carry out a comparative analysis of supply chain performances of leading healthcare organizations in India. In this regard, this paper presents an integrated multi-criteria decision making (MCDM) framework wherein we derive the weights of the criteria based on experts’ opinions using PIvot Pairwise RElative Criteria Importance Assessment (PIPRECIA) method. We then apply three distinct frameworks such as Multi-Attributive Border Approximation area Comparison (MABAC), Combined Compromise Solution (CoCoSo) and Measurement of alternatives and ranking according to COmpromise solution (MARCOS) for ranking purpose. In this context, this paper presents a comparative analysis of the results obtained from these approaches. The results show that large cap firms do not necessarily perform well. Further, the results of three MCDM frameworks demonstrates consistency.

50 citations

Proceedings ArticleDOI
01 Jul 2015
TL;DR: A novel framework for machine translation evaluation using neural networks in a pairwise setting, where the goal is to select the better translation from a pair of hypotheses, given the reference translation.
Abstract: We present a novel framework for machine translation evaluation using neural networks in a pairwise setting, where the goal is to select the better translation from a pair of hypotheses, given the reference translation. In this framework, lexical, syntactic and semantic information from the reference and the two hypotheses is compacted into relatively small distributed vector representations, and fed into a multi-layer neural network that models the interaction between each of the hypotheses and the reference, as well as between the two hypotheses. These compact representations are in turn based on word and sentence embeddings, which are learned using neural networks. The framework is flexible, allows for efficient learning and classification, and yields correlation with humans that rivals the state of the art.

50 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
20231,305
20222,607
2021581
2020554
2019520