no. 3
Integrating decision making conditions into DEA models
RAIRO. Operations Research, Tome 55 (2021) no. 3, pp. 1743-1756

Data Envelopment Analysis (DEA) is a popular non-parametric technique for the assessment of efficiency of a set of homogeneous decision making units (DMUs) with the same set of inputs and outputs. In the conventional DEA models, it is assumed that all variables are fully controllable. However, in the real-world applications of DEA, some of the variables are completely uncontrollable or partially controllable. In this paper, we are concerned about partially controllable variables which are called semi-discretionary variables. In DEA models, in the presence of semi-discretionary variables, decision makers have partial control on these variables and the proportional changes are possible to some extent. Previous DEA models with semi-discretionary variables consider a certain level of control on the variables which is fixed and it is given by decision makers or a higher authority. Since this level is usually given by experts, it is possible that in some cases all experts may not come up with an agreement, so in this paper we consider variable instead of fixed level of control on each semi-discretionary variable. In the presence of semi-discretionary variables, the proportional changes in inputs and out-puts may not be feasible and as a result the obtained target value by conventional DEA models is not achievable for an inefficient DMU. Thus, we propose a bi-objective model to evaluate DMUs when modifying a variable to its target value should be managed by decision makers in a voting system. One of the advantages of the proposed model is including decision making conditions directly into a DEA model.

Reçu le :
Accepté le :
Première publication :
Publié le :
DOI : 10.1051/ro/2021075
Classification : 90B50, 90C90, 90C05, 90C29
Keywords: Data envelopment analysis, decision making, efficiency, semi-discretionary variables 
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Zehi, Rokhsaneh Yousef; Mustafa, Adli. Integrating decision making conditions into DEA models. RAIRO. Operations Research, Tome 55 (2021) no. 3, pp. 1743-1756. doi: 10.1051/ro/2021075

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