By Rolf Fare, Shawna Grosskopf, Dimitris Margaritis
Info Envelopment research (DEA) is frequently ignored in empirical paintings akin to diagnostic exams to figure out no matter if the information conform with know-how which, in flip, is necessary in picking out technical switch, or discovering which different types of DEA versions permit facts changes, together with facing ordinal data.
Advances in information Envelopment Analysis specializes in either theoretical advancements and their purposes into the dimension of effective potency and productiveness progress, similar to its software to the modelling of time substitution, i.e. the matter of ways to allocate assets over the years, and estimating the "value" of a call Making Unit (DMU).
Readership: complex postgraduate scholars and researchers in operations examine and economics with a specific curiosity in construction thought and operations administration.
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Extra info for Advances in Data Envelopment Analysis
N, z k ykm yk m , m = 1, . . , M, 0, k = 1, . . , K k=1 K k=1 zk 8 Similar analysis also applies to the output oriented model. , Grosskopf, S. and Margaritis, D. where xkn and ykm are observed inputs and outputs and the z k are intensity variables, here restricted only to be nonnegative, thus constant returns to scale are imposed as in the original CCR model. Here we are evaluating DMU k . We now investigate the data transformation h( p) = ap together with the input-oriented CCR model. , we introduce h(xkn ) = an x kn , k = 1, .
We have h( p) = dh( po ) · p + h( p o ). 15) 3 See the appendix following this chapter for these calculations. 4 Often these are what are sometimes referred to as categorical or environmental variables and have been addressed in various ways, see for example, Banker and Morey (1986a, 1986b). 5 This section builds on Färe and Grosskopf (2012). 6 These assumptions suffice for h to have an inverse. , Grosskopf, S. and Margaritis, D. 16) our linear approximation of the ordinal data becomes an affine data transformation h( p) = ap + b.
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. Here these additional constraints are strict equalities (rather than inequalities) which imposes weak rather than strong disposability of good and bad outputs. Färe and Grosskopf (2004) introduced the following two conditions on the undesirable output data to accommodate null jointness: K J bkj > 0, (v) k=1 j = 1, .