By Rolf Fare, Shawna Grosskopf, Dimitris Margaritis
Information Envelopment research (DEA) is frequently missed in empirical paintings corresponding to diagnostic assessments to figure out even if the information conform with know-how which, in flip, is critical in selecting technical swap, or discovering which sorts of DEA versions let info differences, together with facing ordinal data.
Advances in information Envelopment Analysis makes a speciality of either theoretical advancements and their functions into the dimension of efficient potency and productiveness progress, akin to its program to the modelling of time substitution, i.e. the matter of the way to allocate assets through the years, and estimating the "value" of a choice Making Unit (DMU).
Readership: complicated postgraduate scholars and researchers in operations examine and economics with a selected curiosity in construction concept and operations administration.
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Additional info for Advances in Data Envelopment Analysis
1) To illustrate, assume that we have two DMUs (K = 2) which employ two inputs (N = 2) to produce two outputs (M = 2). , X= x11 x21 x 12 x22 , Y = y11 y21 y12 y22 The Kemeny, Morgenstern and Thompson (1956) condition from (i) above holds for this simple example if y11 + y21 > 0, March 10, 2015 8:49 Advances in Data Envelopment Analysis - 9in x 6in b2007-ch02 page 19 Chapter 2. Looking at the Data in DEA 19 which also holds for output 2, requiring that one of the outputs must be strictly positive.
3) We note that this can be refined to allow for a subvector of y to be nulljoint with a subvector of bad outputs. To formulate a DEA technology that accommodates undesirable outputs assume that data on such outputs for each DMU k = 1, . . , K are denoted (bk1 , . . , bk J ), k = 1, . . , K . The model now reads K P(x ) = (y, b) : o z k x kn xno , n = 1, . . , N z k ykm ym , m = 1, . . 4) k=1 K k=1 K z k bkj = b j , j = 1, . . , J k=1 zk 0, k = 1, . . , K . This model has j = 1, . . , J additional constraints added to the usual input and output constraints.
Denoting bad outputs by b = (b1 , . . , Grosskopf, S. and Margaritis, D. and null jointness1 between good and bad output is defined as (y, b) ∈ P(x) and b = 0 imply y = 0. 3) We note that this can be refined to allow for a subvector of y to be nulljoint with a subvector of bad outputs. To formulate a DEA technology that accommodates undesirable outputs assume that data on such outputs for each DMU k = 1, . . , K are denoted (bk1 , . . , bk J ), k = 1, . . , K . The model now reads K P(x ) = (y, b) : o z k x kn xno , n = 1, .