By Zhu J., Cook W.D.
In a comparatively brief time period, info Envelopment research (DEA) has grown right into a strong quantitative, analytical device for measuring and comparing functionality. it's been effectively utilized to a complete number of difficulties in lots of assorted contexts world wide. The research of an array of those difficulties has been proof against different methodological methods as a result of the a number of degrees of complexity that needs to be thought of. a number of examples of multifaceted difficulties during which DEA research has been effectively used are: (1) upkeep actions folks Air strength bases in geographically dispersed destinations, (2) coverage strength efficiencies within the uk, (3) department financial institution performances in Canada, Cyprus, and different nations and (4) the potency of universities in acting their schooling and examine capabilities within the united states, England, and France. as well as localized difficulties, DEA functions were prolonged to functionality reviews of 'larger entities' akin to towns, areas, and nations. those extensions have a much wider scope than conventional analyses simply because they contain ''social'' and ''quality-of-life'' dimensions which require the modeling of qualitative and quantitative info with a purpose to study the layers of complexity for an overview of functionality and to supply resolution strategies.DEA is computational at its middle and this ebook can be one of the books that we'll glance to submit at the computational features of DEA. This booklet by way of Zhu and cook dinner will care for the micro facets of dealing with and modeling information concerns in modeling DEA difficulties. DEA's use has grown with its potential of facing complicated ''service industry'' and the ''public provider domain'' kinds of difficulties that require modeling either qualitative and quantitative facts. it will be a instruction manual remedy facing particular facts difficulties together with the next: (1) obscure facts, (2) erroneous information, (3) lacking information, (4) qualitative info, (5) outliers, (6) bad outputs, (7) caliber facts, (8) statistical research, (9) software program and different facts elements of modeling complicated DEA difficulties. moreover, the publication will exhibit the way to visualize DEA effects while the knowledge is greater than three-d, and the way to spot potency devices fast and competently.
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Additional info for Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis
THE CONTINUOUS PROJECTION MODEL AND IDEA Cooper, Park and Yu (1999) examine the DEA structure in the presence of imprecise data (IDEA) for certain factors. Zhu (2003) and others have extended Cooper, Park and Yu’s (1999) earlier model. This is further elaborated in Chapter 3. One particular form of imprecise data is a full ranking of the DMUs in an ordinal sense. Representation of rank data via a Likert scale, with L < N rank positions, can be viewed as a generalization of the Cooper, Park and Yu (1999) structure wherein L = N.
This θ o* can be achieved with (i) xij = x ij for DMU j (j ≠ o); (ii) yrj = y rj for DMU j (j ≠ o); (iii) yro = y ro for DMU o ; (iv) xio = xio for DMU o . [Proof] See Chen (2006). Theorem 3 is true due to the fact that input decreases/output increases in DMU o or (and) input decreases/output increases in other DMUs will deteriorate the efficiency of DMU o . Theorem 4 indicating that EIDEA can be executed by setting the lower output bounds and upper input bounds as the exact output and input values for DMU o and by setting upper output bounds and lower input bounds as the exact output and input values for the remaining DMUs.
L. Seiford and K. Tone. 2000. Data Envelopment Analysis, Kluwer Academic Publishers, Boston. 11. Fare, R. K. Lovell. 1978. Measuring the technical efficiency of production. Journal of Economic Theory 19(1), 150-162. 12. M. 1996. Duality, classification and slacks in DEA. Annals of Operations Research 66, 109-138. 13. Zhu, J. 2003. Imprecise Data Envelopment Analysis (IDEA): A review and improvement with an application. European Journal of Operational Research 144, 513-529. , M. M. Seiford, 1993, “On the use of ordinal data in Data Envelopment Analysis”, Journal of the Operational Research Society 44, 133-140, Cook, W.
Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis by Zhu J., Cook W.D.