Alternative solutions for classifying inputs and outputs in data envelopment analysis

Mehdi Toloo*

*المؤلف المقابل لهذا العمل

نتاج البحث: المساهمة في مجلةمقالمراجعة النظراء

48 اقتباسات (Scopus)

ملخص

In conventional data envelopment analysis (DEA) models, a performance measure whether as an input or output usually has to be known. Nevertheless, in some cases, the type of a performance measure is not clear and some models are introduced to accommodate such flexible measures. In this paper, it is shown that alternative optimal solutions of these models has to be considered to deal with the flexible measures, otherwise incorrect results might occur. Practically, the efficiency scores of a DMU could be equal when the flexible measure is considered either as input or output. These cases are introduced and referred as share cases in this study specifically. It is duplicated that share cases must not be taken into account for classifying inputs and outputs. A new mixed integer linear programming (MILP) model is proposed to overcome the problem of not considering the alternative optimal solutions of classifier models. Finally, the applicability of the proposed model is illustrated by a real data set.

اللغة الأصليةEnglish
الصفحات (من إلى)1104-1110
عدد الصفحات7
دوريةComputers and Mathematics with Applications
مستوى الصوت63
رقم الإصدار6
المعرِّفات الرقمية للأشياء
حالة النشرPublished - مارس 2012
منشور خارجيًانعم

ASJC Scopus subject areas

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