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Graduate School of Business, The University of Texas at Austin, Austin, Texas 78712
Data Envelopment Analysis (DEA) models, as ordinarily employed, assume that the data for all inputs and outputs are known exactly. In some applications, however, a number of factors may involve imprecise data, which take forms such as ordinal rankings and knowledge only of bounds. Here we provide an example involving a Korean mobile telecommunication company. The Imprecise Data Envelopment Analysis (IDEA) method we use permits us to deal not only with imprecise data and exact data but also with weight restrictions as in the (now) widely used "Assurance Region" (AR) and "cone-ratio envelopment" approaches to DEA. We also show how to transform AR bounds on the variables, obtained from managerial assessments, for instance, into data adjustments. This involves an extended IDEA model, which we refer to as AR-IDEA. All these uses are illustrated by an example application directed to evaluate efficiencies of branch offices of a telecommunication company in Korea.
College of Business Administration, University of Ulsan, 29 Mugeo, Ulsan 680-749
Department of MSIS, McCombs School of Business, The University of Texas at Austin, Austin, Texas 78712
cooperw{at}mail.utexas.edu
sampark{at}uou2.ulsan.ac.kr
yu{at}uts.cc.utexas.edu
Subject classifications: Telecommunications: performance evaluation; Organization: employee performance evaluation; Linear programming: applicationsalgorithms.
History: Received July 1998;
revision received May 1999; revision received November 1999;
accepted December 1999.
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