Dea based multiple criteria evaluation and the multivariate statistics analysis for ranking units

Ke Wang, Fajie Wei

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Data envelopment analysis (DEA) method was originally designed to evaluate the efficiency of decision making units (DMUs), and it classifies the units merely into two dichotomic groups: efficient and inefficient ones. The purpose of the paper is to combine the multiple criteria evaluation with DEA and to fully rank the evaluation units from the most efficient one to the least efficient one as classified by DEA. In this paper we develop two new multivariate statistics analysis DEA based models for multiple criteria evaluation. Both of the methods could solve multiple criteria evaluation problem, which includes three types of index (cost, benefit, and fixed), and provide a full ranking of the efficient and inefficient units on the same scale based on the maximal correlation between the linear combinations of inputs and outputs sets where their common weights are computed by maximizing the eigenvalue of a quadruple matrix product; or based on the ratio between the composite output and the composite input where their common weights are computed by a new nonlinear optimization of the goodness of separation between two groups. We also demonstrate this method for evaluation and full ranking through an example.

Original languageEnglish
Title of host publication38th International Conference on Computers and Industrial Engineering 2008
Pages74-82
Number of pages9
Publication statusPublished - 2008
Externally publishedYes
Event38th International Conference on Computers and Industrial Engineering 2008 - Beijing, China
Duration: 31 Oct 20082 Nov 2008

Publication series

Name38th International Conference on Computers and Industrial Engineering 2008
Volume1

Conference

Conference38th International Conference on Computers and Industrial Engineering 2008
Country/TerritoryChina
CityBeijing
Period31/10/082/11/08

Keywords

  • Canonical correlation analysis
  • Data envelopment analysis
  • Discriminant analysis
  • Multiple criteria evaluation
  • Multivariate statistics analysis
  • Ranking

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