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| Number | CSW methods developed | Authors | Year |
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| 1 | Provided a subjective ordinal preference ranking by developing common weights through a series of bounded DEA runs, by closing the gap between the upper and lower limits of the weights. | Cook and Kress [19, 20] | 1990, 1991 |
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| 2 | Used a general unbounded DEA model to obtain different sets of weights and then taking their average or weighted average with DEA efficiencies as the weights, maximizing the average efficiency of DMUs, maximizing the number of DEA efficient units, and ranking various factors by some order of importance and then assigning low weights to less important factors and maximal feasible weights to important ones | Roll et al. [21]
Roll [22] | 1991
1993 |
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| 3 | Considered the common weights for all the units, by maximizing the sum of efficiency ratios of all the units, in order to rank each unit as well as suggesting a potential use of the common weights for ranking DMUs. | Ganley and Cubbin [23] | 1992 |
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| 4 | Developed a two-stage linear discriminate analysis approach to generate the common weights | Sinuany-Stern, et al. [24] | 1994 |
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| 5 | Developed a maxi-min efficiency ratio model which also creates common weights for evaluation | Troutt [25] | 1995 |
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| 6 | Used the canonical correlation analysis to provide a single weight vector for inputs and outputs, respectively, common to all DMUs. | Friedman and Sinuany-Stern [26] | 1997 |
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| 7 | Presented a nonlinear discriminate analysis to provide the common weights for all DMUs. | Sinuany-Stern and Friedman [27] | 1998 |
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| 8 | Presented the multiple objectives max-min model to determine CSW | Chiang and Tzeng [28] | 2000 |
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| 9 | Minimizes a convex combination of these deviations measured in terms of a couple of distances in such family | Despotis [29] | 2002 |
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| 10 | Proposed a DEA-CP (compromise programming) model which aims at seeking a common set of weights across the DMUs by combining the DEA and the compromise programming. | Hashimoto and Wu [30] | 2004 |
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| 11 | Based on multiple objective nonlinear programming and by using compromise solution approach, proposed a method to generate a common set of weights for all DMUs which are able to produce a vector of efficiency scores closest to the efficiency scores calculated from the standard DEA model (ideal solution) | Kao and Hung [31] | 2005 |
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| 12 | Based on multiple objective nonlinear programming and maximization of the minimum value of the efficiency scores, proposed a method to generate a common set of weights for all DMUs. | Jahanshahloo et al. [32] | 2005 |
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| 13 | Developed a goal-programming model for this setting that seeks to derive such a common-multiplier set. The important feature of this multiplier set is that it minimizes the maximum discrepancy among the within-group scores from their ideal levels. And deal with these distances but relax the objective to groups of DMUs which operate in similar circumstances | Cook and Zhu [33] | 2007 |
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| 14 | Used a multiple objective linear programming (MOLP) approach for generating a common set of weights in the DEA framework. | Makui et al. [34] | 2008 |
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| 15 | Proposed a common weights analysis (CWA) methodology to search for a common set of weights for DMUs. | Liu and Peng [18] | 2008 |
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| 16 | Dealt with deviations regarding the total input virtual and the total output virtual | Franklin Liu and Peng [35] | 2009 |
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| 17 | Introduced a minimum weight restriction and as a side effect, common weights are also achieved. Imposed weight restrictions to incorporate value judgment are widely researched within DEA but as these methods originally do not necessarily and purposefully provide a full ranking, they are not explicitly discussed here. | Wang et al. [36] | 2009 |
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| 18 | Proposed two approaches to obtain the set of common weights for ranking efficient DMUs by comparing with an ideal line and the special line. | Jahanshahloo et al. [6] | 2010 |
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| 19 | Proposed a CSW as the average of the profiles of weights provided by the so-called ‘‘neutral’’ model used in the cross-efficiency evaluation. | Wang and Chin [37] | 2010 |
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| 20 | Proposed a common weight MCDA-DEA method with a more discriminating power over the existing ones that enable us to construct CIs using a set of common weights. | Hatefi and Torabi [38] | 2010 |
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| 21 | Used methods based on regression analysis to seek a common set of weights that are easy to estimate and can produce a full ranking for DMUs. | Wang et al. [39] | 2011 |
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| 22 | A separation method is proposed for locating a set of weights, also known as a common set of weights (CSW), in the data envelopment analysis (DEA). | Chiang et al. [40] | 2011 |
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| 23 | Extended a common-weights DEA approach involving a linear programming problem to gauge the efficiency of the DMUs with respect to the multiobjective model. | Davoodi and Rezai. [41] | 2012 |
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| 24 | Used an approach to minimize the deviations of the CSW from the DEA profiles of weights without zeros of the efficient DMUs. This minimization reduces, in particular, the differences between the DEA profiles of weights that are chosen, so the CSW proposed is a representative summary of such DEA weights profiles. Several norms to the measurement of such differences are used. | Ramón et al. [8] | 2012 |
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| 25 | Proposed two models considering ideal and anti-ideal DMU to generate common weights from the view of multiple criteria decision analysis (MADA), for performance evaluation and ranking. | Sun et al. [42] | 2013 |
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