Artikel

A novel best worst method robust data envelopment analysis: Incorporating decision makers' preferences in an uncertain environment

Data Envelopment Analysis (DEA) has been widely applied in measuring the efficiency of Decision-Making Units (DMUs). The conventional DEA has three major drawbacks: a) it does not consider Decision Makers' (DMs) preferences in the evaluation process, b) DMUs in this model are flexible in weighting the criteria to reach the maximum possible efficiency, and c) it ignores the uncertainty in data. However, in many real-world applications, data are uncertain as well as imprecise and managers want to impose their opinions in decision-making procedure. To address these problems, this paper develops a novel multi-objective Best Worst Method (BWM)-Robust DEA (RDEA) for incorporating DMs' preferences into DEA model in an uncertain environment. The proposed model tries to provide a new efficiency score which is more reliable and compatible with real problems by taking the advantages of the BWM to apply experts' opinions and RDEA to model the uncertainty This bi-objective BWM-RDEA model is solved utilizing amin-max technique and so as to illustrate its usefulness, this model is implemented for assessing Iranian airlines.

Sprache
Englisch

Erschienen in
Journal: Operations Research Perspectives ; ISSN: 2214-7160 ; Volume: 8 ; Year: 2021 ; Pages: 1-11 ; Amsterdam: Elsevier

Klassifikation
Wirtschaft
Thema
Best Worst Method (BWM)
Robust Optimization
Data Envelopment Analysis (DEA)
Airline Efficiency

Ereignis
Geistige Schöpfung
(wer)
Omrani, Hashem
Valipour, Mahsa
Emrouznejad, Ali
Ereignis
Veröffentlichung
(wer)
Elsevier
(wo)
Amsterdam
(wann)
2021

DOI
doi:10.1016/j.orp.2021.100184
Handle
Letzte Aktualisierung
10.03.2025, 11:43 MEZ

Datenpartner

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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Omrani, Hashem
  • Valipour, Mahsa
  • Emrouznejad, Ali
  • Elsevier

Entstanden

  • 2021

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