TY - JOUR
T1 - Ranking via composite weighting schemes under a DEA cross-evaluation framework
AU - Oukil, Amar
N1 - Publisher Copyright:
© 2018 Elsevier Ltd
PY - 2018/3
Y1 - 2018/3
N2 - Data envelopment analysis (DEA) is one of the most powerful tools for ranking decision making units (DMUs). In this paper, we present a new perspective for ranking DMUs under a DEA peer-evaluation framework. We exploit the property of multiple weighting schemes generated over the cross evaluation process in developing a methodology that yields not only robust ranking patterns but also more realistic sets of weights for the DMUs. The robustness of the proposed methodology is evaluated using OWA combinations involving different minimax disparity models and different levels of optimism of the decision maker. We show that discrimination is boosted at each stage of the decision process. As an illustration, our approach is applied for ranking a sample of baseball players.
AB - Data envelopment analysis (DEA) is one of the most powerful tools for ranking decision making units (DMUs). In this paper, we present a new perspective for ranking DMUs under a DEA peer-evaluation framework. We exploit the property of multiple weighting schemes generated over the cross evaluation process in developing a methodology that yields not only robust ranking patterns but also more realistic sets of weights for the DMUs. The robustness of the proposed methodology is evaluated using OWA combinations involving different minimax disparity models and different levels of optimism of the decision maker. We show that discrimination is boosted at each stage of the decision process. As an illustration, our approach is applied for ranking a sample of baseball players.
KW - Composite value system
KW - Cross-efficiency
KW - Data envelopment analysis
KW - Ranking
KW - Weighting scheme
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U2 - 10.1016/j.cie.2018.01.022
DO - 10.1016/j.cie.2018.01.022
M3 - Article
AN - SCOPUS:85041710578
SN - 0360-8352
VL - 117
SP - 217
EP - 224
JO - Computers and Industrial Engineering
JF - Computers and Industrial Engineering
ER -