TY - JOUR
T1 - Developing a model for determining optimal η in DEA-discriminant analysis for predicting suppliers’ group membership in supply chain
AU - Boudaghi, Elahe
AU - Saen, Reza Farzipoor
N1 - Publisher Copyright:
© 2014, Operational Research Society of India.
PY - 2015/3
Y1 - 2015/3
N2 - This study aims at developing a model for determining optimal η in data envelopment analysis –discriminant analysis (DEA-DA) in order to predict the suppliers’ group membership in supply chain. Also, this paper improves the DEA-DA model developed by Sueyoshi (Eur. J. Oper. Res. 115(3), 564–582; [11]). In this model, η parameter is used in order to avoid a trivial solution and to create more discrimination. Since the main shortcoming of the DEA-DA is determining η value based on the researcher’s subjective decision, the present paper develops a model to determine optimal η. Consequently, this paper leads to the estimate of an optimal set of weights for producing a hyperplane for determining the discriminant linear function and, hence promotes the prediction precision of the group membership of a set of observations. The efficacy of proposed model is presented using a numerical example as well as a case study.
AB - This study aims at developing a model for determining optimal η in data envelopment analysis –discriminant analysis (DEA-DA) in order to predict the suppliers’ group membership in supply chain. Also, this paper improves the DEA-DA model developed by Sueyoshi (Eur. J. Oper. Res. 115(3), 564–582; [11]). In this model, η parameter is used in order to avoid a trivial solution and to create more discrimination. Since the main shortcoming of the DEA-DA is determining η value based on the researcher’s subjective decision, the present paper develops a model to determine optimal η. Consequently, this paper leads to the estimate of an optimal set of weights for producing a hyperplane for determining the discriminant linear function and, hence promotes the prediction precision of the group membership of a set of observations. The efficacy of proposed model is presented using a numerical example as well as a case study.
KW - DEA-discriminant analysis
KW - Discriminant linear function
KW - Hierarchical cluster analysis
KW - Predicting suppliers’ group membership
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U2 - 10.1007/s12597-014-0173-6
DO - 10.1007/s12597-014-0173-6
M3 - Article
AN - SCOPUS:84924758824
SN - 0030-3887
VL - 52
SP - 134
EP - 155
JO - OPSEARCH
JF - OPSEARCH
IS - 1
ER -