Efficiency analysis technique with input and output satisficing approach based on Type-2 Neutrosophic Fuzzy Sets: A case study of container shipping companies - Zolfani, Sarfaraz Hashemkhani; Faruk Görçün, Ömer; Çanakçıoğlu, Mustafa; Tirkolaee, Erfan Babaee (Expert systems with applications, 2023)
•Developing a novel EATWIOS based on Type-2 Neutrosophic Fuzzy Numbers (T2NFN)•Presenting novel criteria set identified using a mixed approach.•Extending a performance analysis technique using T2NFN sets.•Demonstrating the validity, robustness, and applicability of the proposed T2NFN approach. This work tries to discuss and evaluate the advantages and superiorities of the extended Efficiency Analysis Technique with Input and Output Satisficing (EATWIOS) method based on Type-2 Neutrosophic Fuzzy Numbers (T2NFNs). The suggested model is maximally stable and robust by considering sensitivity analysis results which demonstrates a new performance analysis approach based on T2NFN sets. The proposed model deals with the input and output criteria and considers existing uncertainties arising from insufficient information and the dynamic structure of the industries. The model's basic algorithm has a unique structure compared to the previous performance analysis technique, and it does not require applying additional weighting techniques to identify the criteria weights. To the best of our knowledge, the extended version of the EATWIOS technique based on the T2NFN set is presented for the first time. The developed model provides reasonable and logical results to practitioners because it deals with satisfactory outputs instead of optimal outputs. This model is an immensely strengthened version of the EATWIOS technique, as the T2NFN sets treat predictable and unpredictable uncertainties. The suggested T2NFN-EATWIOS is then applied to a real-world assessment problem in the container shipping industry. The obtained results are pretty reasonable and logical. Moreover, the results of a comprehensive sensitivity analysis with three stages approve the robustness of the suggested model.