Sample median approximation on stochastic data envelopment analysis

Nasution M. D., Mawengkang H., Kamil A. A., Efendi S., Sutarman S.

International Journal of Agile Systems and Management, cilt.13, sa.3, ss.279-295, 2020 (Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 13 Sayı: 3
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1504/ijasm.2020.109242
  • Dergi Adı: International Journal of Agile Systems and Management
  • Derginin Tarandığı İndeksler: Scopus, Aerospace Database, Communication Abstracts, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.279-295
  • Anahtar Kelimeler: Banker, Charnes and Cooper model, Data envelopment analysis, Decision making unit, Deterministic data envelopment analysis, Efficiency, Level of aspiration, Level of risk, Sample median approximation, SDEA, SMA, Stochastic data envelopment analysis, Super efficiency
  • İstanbul Gelişim Üniversitesi Adresli: Evet

Özet

© 2020 Inderscience Enterprises Ltd.. All rights reserved.This paper study a new approximation model to solving stochastic data envelopment analysis (SDEA) problem. The proposed approach is based on problems that might occur in everyday life. This paper discusses the approach in determining the efficiency and super efficiency ratings of a decision making unit (DMU) in the DEA model with stochastic data. In determining efficiency, SDEA is first transformed into an equivalent deterministic DEA by changing its chance constraints in such a way that the SDEA problem can be solved easily. The author proposes an approach technique called a sample median approximation (SMA) to change the chance constraints so that it will be easy to get the optimal solution in determining the efficiency of DMUs. In the process, the data to be processed first is determined by the median average which will later be considered to represent the actual sample average. As a numerical example, the author resolves the vendor selection problem as presented by Wu and Olson (2006) in their paper. By taking the same parameter value (a = 0.2 and beta = 0.9), the efficiency score and super efficiency of the problem are obtained.