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DEA cross-efficiency framework for efficiency evaluation with probabilistic linguistic term sets

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  • Ling Pan
  • Zeshui Xu
  • Peijia Ren

Abstract

Data envelopment analysis (DEA) is widely used in various practical problems as a general framework for efficiency-evaluation problems by containing the input-output data. With the increasingly complex factors in practice, portraying the uncertainty in problems is necessary for ensuring the reasonableness of results. As the probabilistic linguistic term set (PLTS) is a powerful tool for depicting uncertain information comprehensively, we aim to propose a DEA cross-efficiency framework for efficiency evaluation under probabilistic linguistic environment, which includes (1) defining the preference-based expectation function of a PLTS, (2) establishing the probabilistic linguistic DEA model, (3) developing an algorithm based on the dual form of the probabilistic linguistic DEA model, and (4) building the positive ideal-seeking cross-efficiency model. Furthermore, simulation tests are made to provide guidance for decision makers on the value assignment in practical efficiency-evaluation problems. A case study is conducted to verify the applicability of the proposed framework.

Suggested Citation

  • Ling Pan & Zeshui Xu & Peijia Ren, 2021. "DEA cross-efficiency framework for efficiency evaluation with probabilistic linguistic term sets," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 72(5), pages 1191-1206, May.
    • Handle: RePEc:taf:tjorxx:v:72:y:2021:i:5:p:1191-1206
    DOI: 10.1080/01605682.2020.1848360
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    Cited by:

    1. Jin, Feifei & Cai, Yuhang & Zhou, Ligang & Ding, Tao, 2023. "Regret-rejoice two-stage multiplicative DEA models-driven cross-efficiency evaluation with probabilistic linguistic information," Omega, Elsevier, vol. 117(C).
    2. Zhao, Meng & Xu, Chang & Zhao, Wenxian & Lin, Mingwei, 2023. "New energy vehicle online selection method considering attribute compensation relationship and aspiration strength," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).

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