Study Of The Optimization Of Measurenment Efficiency Model Of Fuzzy Data Envelopment Analysis
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Published:
Jul 30, 2022
Keywords:
Efficiency, performance, Data envelopment analysis (DEA), Fuzzy DEA
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Computer Science
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Abstract
The performance of determination efficiency is affected by both situation certainty and uncertainty. The goal of this study is to develop fuzzy data envelopment analysis models for measuring efficiency performance with the Data envelopment analysis (DEA) method and also a fuzzy method that considers the inputs and outputs of the decision making unit (DMU) on intuitionistic triangular fuzzy numbers. On model development, a step procedure in measurement efficiency performance is given. For processing its efficiency, there is input and output representation on useful intuitionistic triangular fuzzy numbers as output and input estimation data. The application of the fuzzy model to the given case demonstrates that it can be used and is capable of measuring efficiency performance performance more efficiently.
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How to Cite
Vinsensia, D., Yulia Utami, Awaluddin Fitra, Rina Nanda Hanifa and Mestriawan Barasa (2022) “Study Of The Optimization Of Measurenment Efficiency Model Of Fuzzy Data Envelopment Analysis”, Jurnal Mantik, 6(2), pp. 1929-1937. doi: 10.35335/mantik.v6i2.2734.
References
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[16] D. Vinsensia and Y. Utami, “Study Of Fuzzy Goal Programming Model To Production Planning Problems Approach,” J. Tek. Inform. C.I.T Medicom, vol. 13, no. 2, pp. 75–81, 2021, doi: 10.35335/cit.vol13.2021.149.pp75-81.
[2] Y. C. Zhu and S. K. Au, “Bayesian data driven model for uncertain modal properties identified from operational modal analysis,” Mech. Syst. Signal Process., vol. 136, p. 106511, 2020, doi: 10.1016/j.ymssp.2019.106511.
[3] T. C. C. Nepomuceno, W. M. N. Silva, K. T. C. Nepomuceno, and I. K. F. Barros, “A DEA-Based Complexity of Needs Approach for Hospital Beds Evacuation during the COVID-19 Outbreak,” J. Healthc. Eng., vol. 2020, p. 8857553, 2020, doi: 10.1155/2020/8857553.
[4] M. Bagheri, A. Ebrahimnejad, S. Razavyan, F. Hosseinzadeh Lotfi, and N. Malekmohammadi, Fuzzy arithmetic DEA approach for fuzzy multi-objective transportation problem, vol. 22, no. 2. Springer Berlin Heidelberg, 2022. doi: 10.1007/s12351-020-00592-4.
[5] M. Bagheri, A. Ebrahimnejad, S. Razavyan, F. Hosseinzadeh Lotfi, and N. Malekmohammadi, “Solving the fully fuzzy multi-objective transportation problem based on the common set of weights in DEA,” J. Intell. Fuzzy Syst., vol. 39, no. 3, pp. 3099–3124, 2020, doi: 10.3233/JIFS-191560.
[6] A. Ashrafi and M. Mansouri Kaleibar, “Cost, Revenue and Profit Efficiency Models in Generalized Fuzzy Data Envelopment Analysis,” Fuzzy Inf. Eng., vol. 9, no. 2, pp. 237–246, 2017, doi: 10.1016/j.fiae.2017.06.007.
[7] I. Giyanti and A. Indrasari, “Model Pengukuran Efisiensi Perusahaan Berbasis Fuzzy Data Envelopment Analysis,” in Seminar dan Konferensi Nasional IDEC, 2018, pp. 7–8. [Online]. Available: https://idec.ft.uns.ac.id/wp-content/uploads/2018/05/ID049.pdf
[8] S. Bray, L. Caggiani, and M. Ottomanelli, “Measuring transport systems efficiency under uncertainty by fuzzy sets theory based data envelopment analysis: Theoretical and practical comparison with traditional DEA model,” Transp. Res. Procedia, vol. 5, pp. 186–200, 2015, doi: 10.1016/j.trpro.2015.01.005.
[9] S. Bray, L. Caggiani, M. Dell’Orco, and M. Ottomanelli, “Measuring Transport Systems Efficiency under Uncertainty by Fuzzy Sets Theory based Data Envelopment Analysis,” Procedia - Soc. Behav. Sci., vol. 111, no. November, pp. 770–779, 2014, doi: 10.1016/j.sbspro.2014.01.111.
[10] A. Tharwat, B. El-Demerdash, and I. El-Khodary, Developing an Input Oriented Data Envelopment Analysis Model with Fuzzy Uncertainty in Variables. Springer International Publishing, 2019. doi: 10.1007/978-3-030-01662-3_13.
[11] J. Puri and S. P. Yadav, “Intuitionistic fuzzy data envelopment analysis: An application to the banking sector in India,” Expert Syst. Appl., vol. 42, no. 11, pp. 4982–4998, 2015, doi: 10.1016/j.eswa.2015.02.014.
[12] S. A. Edalatpanah, “A Data Envelopment Analysis Model with,” Int. J. Data Envel. Anal., vol. 7, no. 4, pp. 47–58, 2019.
[13] W. D. Cook, K. Tone, and J. Zhu, “Data envelopment analysis: Prior to choosing a model,” Omega (United Kingdom), vol. 44, pp. 1–4, 2014, doi: 10.1016/j.omega.2013.09.004.
[14] P. Peykani, E. Mohammadi, A. Emrouznejad, M. S. Pishvaee, and M. Rostamy-Malkhalifeh, “Fuzzy data envelopment analysis: An adjustable approach,” Expert Syst. Appl., vol. 136, pp. 439–452, 2019, doi: 10.1016/j.eswa.2019.06.039.
[15] J. Puri and S. P. Yadav, “A concept of fuzzy input mix-efficiency in fuzzy DEA and its application in banking sector,” Expert Syst. Appl., vol. 40, no. 5, pp. 1437–1450, 2013, doi: 10.1016/j.eswa.2012.08.047.
[16] D. Vinsensia and Y. Utami, “Study Of Fuzzy Goal Programming Model To Production Planning Problems Approach,” J. Tek. Inform. C.I.T Medicom, vol. 13, no. 2, pp. 75–81, 2021, doi: 10.35335/cit.vol13.2021.149.pp75-81.
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