Two-stage common weight DEA-Based approach for performance evaluation with imprecise data

GÖKER MUTLU N., KARSAK E. E.

SOCIO-ECONOMIC PLANNING SCIENCES, vol.74, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 74
  • Publication Date: 2021
  • Doi Number: 10.1016/j.seps.2020.100943
  • Journal Name: SOCIO-ECONOMIC PLANNING SCIENCES
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, International Bibliography of Social Sciences, Business Source Elite, Business Source Premier, EconLit, Educational research abstracts (ERA), INSPEC, Political Science Complete, Public Affairs Index, Social services abstracts, Sociological abstracts, Worldwide Political Science Abstracts
  • Keywords: Data envelopment analysis, Performance evaluation, Common assessment, Imprecise data, Expatriation
  • Galatasaray University Affiliated: Yes

Abstract

A multi-criteria decision making approach based on data envelopment analysis (DEA) is presented to identify the best performing decision making unit (DMU) accounting for multiple inputs and multiple outputs with the presence of imprecise data. The developed ?-cut based two-stage mathematical programming approach, which yields feasible solutions for all ?-cut levels, generates common set of weights for inputs and outputs, and thus, provides more practical and realistic performance assessment of DMUs. A single rank-order is obtained through OWA operator that is employed for aggregating the efficiency scores regarding ?-levels for each DMU. The robustness of the developed methodology is illustrated by examples taken from earlier research studies along with a case study that is conducted to aid an expatriate to identify the most desirable country in terms of quality of living. The proposed approach provides a ranking with improved discriminating power and enhanced weight dispersion with regard to inputs and outputs while also guaranteeing to determine a single best performing DMU.