A comprehensive neutrosophic model for evaluating the efficiency of airlines based on SBM model of network DEA
DOI:
https://doi.org/10.31181/dma622023729Keywords:
Neutrosophic set, Network DEA, Intermediate product, SBM, Efficiency scoreAbstract
This research aims to provide an innovative framework for evaluating airline performance through the networking of decision-making units (DMUs) and neutrosophic data. As a consequence, we propose a comprehensive methodology for assessing the efficiency of these decision-making units. Network data envelopment analysis (DEA) models deal with measurements of relative efficiency of DMUs when the insight of their internal structures is available. In network models, sub-processes are connected by links or intermediate products. Links have the dual role of output from one division or sub-process and input to another one. Therefore, improving the efficiency score of one division by increasing its output may reduce the score of another division because of increasing its input. To address this conflict, we proposed a new approach in Slack-Based Measure (SBM) framework and neutrosophic logic, which provide deeper insights regarding the sources of inefficiency. Our approach is a new network model, in which the intermediate products are classified into two groups of “input-type”, and “output-type” that their excesses and shortfalls directly concern with the objective function. The results of the illustrated case and analysis show that the constructed model may successfully produce performance assessments. By taking into account the number of slacks or surpluses of these products in the objective function, the proposed model gives analysts and managers a more accurate assessment of network structure efficiency. In this paper, we present a new model in the field of network data envelopment analysis with SBM approach in a neutrosphonic environment, in which for the first time the nature of intermediate products in terms of input or output is investigated. Also, the proposed framework is the first attempt to performance evaluation in neutrosophic network DEA.
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References
Abdelfattah, W. (2019). Data envelopment analysis with neutrosophic inputs and outputs. Expert Systems, 36(6). https://doi.org/10.1111/exsy.12453
Abdelfattah, W. (2021). A parametric approach to solve neutrosophic linear programming models. Journal of Information and Optimization Sciences, 42(3), 631–654. https://doi.org/10.1080/02522667.2020.1764695
Akram, M., Naz, S., & Smarandache, F. (2019). Generalization of maximizing deviation and TOPSIS method for MADM in simplified neutrosophic hesitant fuzzy environment. Symmetry, 11(8), 1058. https://doi.org/10.3390/SYM11081058
Arya, A., & Yadav, S. P. (2020). Performance efficiency of public health sector using intuitionistic fuzzy DEA. International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems, 28(2), 289–315. https://doi.org/10.1142/S0218488520500129
Atanassov, K. T. (2016). Intuitionistic fuzzy sets. In International Journal Bioautomation (Vol. 20). Springer. https://doi.org/10.1007/978-3-7908-1870-3_1
Bagherzadeh Valami, H., & Raeinojehdehi, R. (2016). Ranking units in data envelopment analysis with fuzzy data. Journal of Intelligent and Fuzzy Systems, 30(5), 2505–2516. https://doi.org/10.3233/IFS-151756
Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science. https://doi.org/10.1287/mnsc.30.9.1078
Charnes, A., Cooper,W.W., & Rhodes, E. L. (1978). Measuring the efficiency of decision-making units. European Journal of Operational Research, 2, 429–444.
Chen, Y., Cook, W. D., & Zhu, J. (2014). Additive efficiency decomposition in network DEA. In International Series in Operations Research and Management Science (Vol. 208, pp. 91–118). Springer New York LLC. https://doi.org/10.1007/978-1-4899-8068-7_5
Chen, Z. S., Zhang, X., Govindan, K., Wang, X. J., & Chin, K. S. (2021). Third-party reverse logistics provider selection: A computational semantic analysis-based multi-perspective multi-attribute decision-making approach. Expert Systems with Applications, 166. https://doi.org/10.1016/j.eswa.2020.114051
Cook, W. D., Liang, L., & Zhu, J. (2010). Measuring performance of two-stage network structures by DEA: A review and future perspective. In Omega (Vol. 38, Issue 6, pp. 423–430). https://doi.org/10.1016/j.omega.2009.12.001
Daneshvar Rouyendegh, B. (2011). The DEA and intuitionistic fuzzy TOPSIS approach to departments’ performances: A pilot study. Journal of Applied Mathematics, 2011. https://doi.org/10.1155/2011/712194
Davoudabadi, R., Mousavi, S. M., & Mohagheghi, V. (2021). A new decision model based on DEA and simulation to evaluate renewable energy projects under interval-valued intuitionistic fuzzy uncertainty. Renewable Energy, 164, 1588–1601. https://doi.org/10.1016/j.renene.2020.09.089
Dhar, M. (2021). Neutrosophic soft matrices and its application in medical diagnosis. Journal of Fuzzy Extension and Applications, 2(1), 23–32. https://doi.org/10.22105/jfea.2021.246237.1000
Edalatpanah, S. A. (2018). Neutrosophic perspective on DEA. Journal of Applied Research on Industrial Engineering, 5(4), 339–345. https://doi.org/10.22105/jarie.2019.196020.1100
Edalatpanah, S. A. (2019). A data envelopment analysis model with triangular intuitionistic fuzzy numbers. International Journal of Data Envelopment Analysis, 7(4), 47–58.
Edalatpanah, S. A. (2020a). A direct model for triangular neutrosophic linear programming. International Journal of Neutrosophic Science, 1(1), 19–28. https://doi.org/10.5281/zenodo.3679499
Edalatpanah, S. A. (2020b). Data envelopment analysis based on triangular neutrosophic numbers. CAAI Transactions on Intelligence Technology, 5(2), 94–98. https://doi.org/10.1049/trit.2020.0016
Edalatpanah, S. A., & Smarandache, F. (2019). Data envelopment analysis for simplified neutrosophic Sets. Neutrosophic Sets and Systems, 29, 215–226. https://doi.org/https://digitalrepository.unm.edu/nss_journal/vol29/iss1/17
Eyo, I., Eyoh, J., & Umoh, U. (2021). On the prediction of COVID-19 time series: an intuitionistic fuzzy logic approach. Journal of Fuzzy Extension and Applications, 2(2), 171–190. https://doi.org/https://doi.org/10.22105/jfea.2021.263890.1070
Eyo, I. J., Adeoye, O. S., Inyang, U. G., & ... (2022). Hybrid intelligent parameter tuning approach for COVID-19 time series modeling and prediction. Journal of Fuzzy Extension and Applications. https://doi.org/10.22105/jfea.2021.309332.1164
Färe, R., & Groskopf, S. (2000). Network DEA. In Socio-Economic Planning Sciences (Vol. 34, Issue 1). https://doi.org/https://doi.org/10.1016/S0038-0121(99)00012-9
Färe, R., Grosskopf, S., & Whittaker, G. (2007). Network DEA. In Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis (pp. 209–240). Springer US. https://doi.org/10.1007/978-0-387-71607-7_12
Fukuyama, H., & Weber, W. L. (2010). A slacks-based inefficiency measure for a two-stage system with bad outputs. Omega, 38(5), 398–409. https://doi.org/10.1016/j.omega.2009.10.006
Garg, H. (2022). SVNMPR: A new single-valued neutrosophic multiplicative preference relation and their application to decision-making process. International Journal of Intelligent Systems, 37(3), 2089–2130. https://doi.org/10.1002/int.22767
Garg, K., Kannan, D., Diabat, A., & Jha, P. C. (2015). A multi-criteria optimization approach to manage environmental issues in closed loop supply chain network design. Journal of Cleaner Production, 100, 297–314. https://doi.org/10.1016/j.jclepro.2015.02.075
Kahraman, C., Otay, İ., Öztayşi, B., & Onar, S. Ç. (2019). An integrated AHP & DEA methodology with neutrosophic sets. In Studies in Fuzziness and Soft Computing (Vol. 369, pp. 623–645). Springer Verlag. https://doi.org/10.1007/978-3-030-00045-5_24
Kao, C., & Hwang, S. N. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185(1), 418–429. https://doi.org/10.1016/j.ejor.2006.11.041
Khoshandam, L., & Nematizadeh, M. (2023). An inverse network DEA model for two-stage processes in the presence of undesirable factors. Journal of Applied Research on Industrial Engineering. https://doi.org/https://doi.org/10.22105/jarie.2023.351161.1492
Kumar Das, S. (2020). Application of transportation problem under pentagonal neutrosophic environment. Journal of Fuzzy Extension and Applications, 1(1), 27–40. https://doi.org/10.22105/jfea.2020.246633.1001
Lewis, H. F., Mallikarjun, S., & Sexton, T. R. (2013). Unoriented two-stage DEA: The case of the oscillating intermediate products. European Journal of Operational Research, 229(2), 529–539. https://doi.org/10.1016/j.ejor.2013.02.058
Lewis, L. F., & Sexton, T. R. (2004). Network DEA: Efficiency analysis of organizations with complex internal structure. Computers and Operations Research, 31(9), 1365–1410. https://doi.org/10.1016/S0305-0548(03)00095-9
Liang, L., Cook, W. D., & Zhu, J. (2008). DEA models for two-stage processes: Game approach and efficiency decomposition. Naval Research Logistics, 55(7), 643–653. https://doi.org/10.1002/nav.20308
Lozano, S. (2015). Alternative SBM Model for Network DEA. Computers and Industrial Engineering, 82, 33–40. https://doi.org/10.1016/j.cie.2015.01.008
Mao, X., Guoxi, Z., Fallah, M., Edalatpanah, S. A., & Garg, H. (2020). A Neutrosophic-Based Approach in Data Envelopment Analysis with Undesirable Outputs. Mathematical Problems in Engineering, 2020. https://doi.org/10.1155/2020/7626102
Monzeli, A., Daneshian, B., Tohidi, G., Sanei, M., & Razavian, S. (2020). Efficiency study with undesirable inputs and outputs in DEA. Journal of Fuzzy Extension and Applications, 1(1), 73–80. https://doi.org/10.22105/jfea.2020.248018.1005
Paradi, J. C., Rouatt, S., & Zhu, H. (2011). Two-stage evaluation of bank branch efficiency using data envelopment analysis. Omega, 39(1), 99–109. https://doi.org/10.1016/j.omega.2010.04.002
Pastor, J. T., Ruiz, J. L., & Sirvent, I. (1999). Enhanced DEA Russell graph efficiency measure. European Journal of Operational Research, 115(3), 596–607. https://doi.org/10.1016/S0377-2217(98)00098-8
Sexton, T. R., & Lewis, H. F. (2003). Two-Stage DEA: An Application to Major League Baseball. Journal of Productivity Analysis, 19(2–3), 227–249. https://doi.org/10.1023/A:1022861618317
Shamsijamkhaneh, A., Hadjimolana, S. M., Rahmani Parchicolaie, B., & Hosseinzadehlotfi, F. (2018). Incorporation of Inefficiency Associated with Link Flows in Efficiency Measurement in Network DEA. Mathematical Problems in Engineering, 2018. https://doi.org/10.1155/2018/9470236
Smarandache, F. (1999). A Unifying Field in Logics: Neutrosophic Logic, Neutrosophy, Neutrosophic Set, Neutrosophic Probability. In American Research Press. https://doi.org/https://doi.org/10.22105/jfea.2020.248018.1005
Tavassoli, M., & Saen, R. F. (2022). A new fuzzy network data envelopment analysis model for measuring efficiency and effectiveness: assessing the sustainability of railways. Applied Intelligence, 52(12), 13634–13658. https://doi.org/10.1007/s10489-022-03336-3
Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis,. European Journal of Operational Research, 130, 498–509. https://doi.org/A slacks-based measure of efficiency in data envelopment analysis
Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European Journal of Operational Research, 197(1), 243–252. https://doi.org/https://doi.org/10.1016/j.ejor.2008.05.027
Ucal Sari, I., & Ak, U. (2022). Machine efficiency measurement in industry 4.0 using fuzzy data envelopment analysis. Journal of Fuzzy Extension and Applications, 3(2), 177–191. https://doi.org/10.22105/JFEA.2022.326644.1199
Veeramani, C., Edalatpanah, S. A., & Sharanya, S. (2021). Solving the Multiobjective Fractional Transportation Problem through the Neutrosophic Goal Programming Approach. Discrete Dynamics in Nature and Society, 2021. https://doi.org/10.1155/2021/7308042
Wang, H., Smarandache, F., Sunderraman, R., & Zhang, Y.-Q. (2010). Single valued neutrosophic sets. Multispace and Multistructure, 4(1), 410–413. https://doi.org/https://doi.org/10.1016/j.ejor.2008.05.027
Wen, M., Yu, X., & Wang, F. (2020). A new uncertain DEA model and application to scientific research personnel. Soft Computing, 24(4), 2841–2847. https://doi.org/10.1007/s00500-019-04555-6
Yang, W., Cai, L., Edalatpanah, S. A., & Smarandache, F. (2020). Triangular single valued neutrosophic data envelopment analysis: Application to hospital performance measurement. Symmetry, 12(4). https://doi.org/10.3390/SYM12040588
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/https://doi.org/10.1016/S0019-9958(65)90241-X
Zha, Y., Liang, L., & Xu, C. Y. (2008). Two-stage BCC model for cooperative efficiency evaluation using a geometric mean method. Xitong Gongcheng Lilun Yu Shijian/System Engineering Theory and Practice, 28(10). https://doi.org/10.1016/s1874-8651(10)60001-4
Zhan, J., Akram, M., & Sitara, M. (2019). Novel decision-making method based on bipolar neutrosophic information. Soft Computing, 23(20), 9955–9977. https://doi.org/10.1007/s00500-018-3552-8
Zhang, R., Wei, Q., Li, A., & Chen, S. S. (2022). A new intermediate network data envelopment analysis model for evaluating China’s sustainability. Journal of Cleaner Production, 356. https://doi.org/10.1016/j.jclepro.2022.131845
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