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@article{Amiri_Rostamy-Malkhalifeh_Farhad Hosseinzadeh Lotfi_Mozaffari_2023, title={Measuring returns to scale based on the triangular fuzzy DEA approach with different views of experts: Case study of Iranian insurance companies}, volume={6}, url={https://dmame-journal.org/index.php/dmame/article/view/740}, DOI={10.31181/dmame622023740}, abstractNote={<p class="DMAMEabstrac">The importance of insurance companies in the economic growth of countries has led to them, so in this article, the efficiency of insurance companies is measured based on inputs, favorable and unfavorable outputs. The developed model, unlike the previous models, considers the unfavorable outputs of insurance companies in conditions of uncertainty with fuzzy data based on different views of experts. The required data for each of the inputs and outputs have been provided by experts in the form of triangular fuzzy numbers. The existence of different views of experts, including optimistic, likely, and pessimistic, has led to its impact on the returns to the scale of insurance companies. The results of the survey of 24 insurance companies in Iran, based on the different views of experts, show that the more optimistic the experts’ view is, the higher the average return on the scale of insurance companies compared to other views. As the expert view has shifted from optimistic to pessimistic, returns to full scale for insurance companies have declined. In this way, the average return to the scale of all insurance companies is equal to 0.8972 in the optimistic view, in the probable view it is equal to 0.8863 and in the pessimistic view it is equal to 0.8336. The uncertainty rate also affects the inputs, desirable and undesirable outputs of the model, and with the increase of this rate, the desirable inputs and outputs decrease and the undesirable outputs increase. The result of this is the reduction of the average return to the scale of insurance companies with the increase of the uncertainty rate.</p>}, number={2}, journal={Decision Making: Applications in Management and Engineering}, author={Amiri, Mehdi and Rostamy-Malkhalifeh, Mohsen and Farhad Hosseinzadeh Lotfi and Mozaffari, Mohammadreza}, year={2023}, month={Aug.}, pages={787–807} }