AN EXTENDED SINGLE-VALUED NEUTROSOPHIC AHP AND MULTIMOORA METHOD TO EVALUATE THE OPTIMAL TRAINING AIRCRAFT FOR FLIGHT TRAINING ORGANIZATIONS

Çağlar Karamaşa, Darjan Karabasevic, Dragisa Stanujkic, Alireza Rezanezhad Kookhdan, Arunodaya Raj Mishra, Mehmet Ertürk

DOI Number
10.22190/FUME210521059K
First page
555
Last page
578

Abstract


Aircraft’s training is crucial for a flight training organization (FTO). Therefore, an important decision that these organizations should wisely consider the choice of aircraft to be bought among many alternatives. The criteria for evaluating the optimal training aircraft for FTOs are collected based on the survey approach. Single valued neutrosophic sets (SVNS) have the degree of truth, indeterminacy, and falsity membership functions and, as a special case, neutrosophic sets (NS) deal with inconsistent environments. In this regard, this study has extended a single-valued neutrosophic analytic hierarchy process (AHP) based on multi-objective optimization on the basis of ratio analysis plus a full multiplicative form (MULTIMOORA) to rank the training aircraft as the alternatives. Moreover, a sensitivity analysis is performed to demonstrate the stability of the developed method. Finally, a comparison between the results of the developed approach and the existing approaches for validating the developed approach is discussed. This analysis shows that the proposed approach is efficient and with the other methods.

Keywords

Training Aircraft Selection, Neutrosophic Sets, Flight Training Organization, Civil Aviation, AHP, MULTIMOORA

Full Text:

PDF

References


Dožić, S., Lutovac, T., Kalić, M., 2018, Fuzzy AHP approach to passenger aircraft type selection, Journal of Air Transport Management, 68, pp. 165-175.

Kenan, N., Jebali, A., Diabat, A., 2018, The integrated aircraft routing problem with optional flights and delay considerations, Transportation Research Part E: Logistics and Transportation Review, 118, pp. 355-375.

Liang, Z., Xiao, F., Qian, X., Zhou, L., Jin, X., Lu, X., Karichery, S., 2018, A column generation-based heuristic for aircraft recovery problem with airport capacity constraints and maintenance flexibility, Transportation Research Part B: Methodological, 113, pp. 70-90.

Maywald, J.D., Reiman, A.D., Overstreet, R.E., Johnson, A.W., 2019, Aircraft selection modeling: a multi-step heuristic to enumerate airlift alternatives, Annals of Operations Research, 274, pp. 425-445.

Zhang, J., Zhao, P., Zhang, Y., Dai, X., Sui, D., 2020, Criteria selection and multi-objective optimization of aircraft landing problem, Journal of Air Transport Management, 82, 101734.

Sánchez-Lozano, J.M., Rodríguez, O.N., 2020, Application of Fuzzy Reference Ideal Method (FRIM) to the military advanced training aircraft selection, Applied Soft Computing, 88, 106061.

Wang, T.C., Chang, T.H., 2007, Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment, Expert Systems with Applications, 33(4), pp. 870-880.

Dožić, S.,Kalić, M., 2014, An AHP approach to aircraft selection process, Transportation Research Procedia, 3, pp. 165-174.

Hunter, D.R., Burke, E.F., 1994, Predicting aircraft pilot-training success: A meta-analysis of published research,The International Journal of Aviation Psychology, 4(4), pp. 297-313.

Dožić, S., Kalić, M., 2015, Comparison of two MCDM methodologies in aircraft type selection problem,Transportation Research Procedia, 10, pp. 910-919.

Gomes, L.F.A.M., Mattos Fernandes, J.E., Mello, J.C.C.B.S., 2014, A fuzzy stochastic approach to the multicriteria selection of an aircraft for regional chartering, Journal of Advanced Transportation, 48(3), pp. 223-237.

Sun, X., Gollnick, V., Stumpf, E. 2011, Robustness Consideration in Multi‐Criteria Decision Making to an Aircraft Selection Problem, Journal of Multi‐Criteria Decision Analysis, 18(1-2), pp. 55-64.

Ilgın, M.A., 2019, Aircraft Selection Using Linear Physical Programming, Journal of Aeronautics and Space Technologies, 12(2), pp. 121-128.

Yin, A., Gao, Z., Zhuanga, D., 2019, Discussion on Supplier Selection in the Selection of Large Civil Passenger Aircraft,Journal of Physics: Conference Series, 1187(5), 052007.

Smarandache, F., 1998, Neutrosophy:Neutrosophic Probability, Set, and Logic: Analytic Synthesis & Synthetic Analysis, American Research Press.

See, T.K., Lewis, K., 2002, Multiattribute decision making using hypothetical equivalents, In ASME 2002 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, pp. 401-410.

Yeh, C.H., Chang, Y.H., 2009, Modeling subjective evaluation for fuzzy group multicriteria decision making, European Journal of Operational Research, 194(2), pp. 464-473.

Özdemir, Y., Başligil, H., Karaca, M., 2011, Aircraft selection using analytic network process: A case for Turkish airlines, Proceedings of the World Congress on Engineering, vol.2, pp.1155-1159.

Bruno, G., Esposito, E., Genovese, A., 2015, A model for aircraft evaluation to support strategic decisions, Expert Systems with Applications, 42(13), pp. 5580-5590.

Kannan, D.,deSousa Jabbour, A.B.L.,Jabbour, C.J.C., 2014, Selecting green suppliers based on GSCM practices: Using fuzzy TOPSIS applied to a Brazilian electronics company, European Journal of operational research, 233(2), pp. 432-447.

Lozano, J.M.S., Serna, J., Payan, A.D., 2015, Evaluating military training aircrafts through the combination of multi-criteria decision making processes with fuzzy logic. A case study in the Spanish Air Force Academy, Aerospace Science and Technology, 42, pp. 58-65.

Gürbüz, F., Göleç, A., Şenyiğit, E., 2016, Determination of best military cargo aircraft with multi criteria decision making techniques, MANAS Sosyal Araştırmalar Dergisi, 5(5), pp. 87-101.

Özdemir, Y., Başlıgil, H., 2016, Aircraft selection using fuzzy ANP and the generalized choquet integral method: The Turkish airlines case, Journal of Intelligent & Fuzzy Systems, 31(1), pp. 589-600.

Yurdusevimli, E., Özgen, A., 2017, Choosing the best training aircraft for a flight training organization by multi criteria decision making methods, The Online Journal of Science and Technology, 7(4), pp. 47-51.

Kiracı, K., Bakır, M., 2018, Application of commercial aircraft selection in aviation industry through multi-criteria decision making methods, MCBU Sosyal Bilimler Dergisi, 16(4), pp. 307-332.

Durmaz, K.İ., Gencer, C., 2020, A new plugin based on JSMAA:SWARA-JSMAA and aerobatic aircraft selection, Journal of the Faculty of Engineering and Architecture of Gazi University, 35(3), pp. 1487-1498.

Kiracı, K., Akan, E., 2020, Aircraft selection by applying AHP and TOPSIS in interval type-2 fuzzy sets, Journal of Air Transport Management, 89, 101924.

Ahmed, S.K., Sivakumar, G., Kabir, G., Ali, S.M., 2020, Regional aircraft selection integrating fuzzy analytic hierarchy process (FAHP) and efficacy method, Journal of Production Systems & Manufacturing Science, 1(2), pp. 63–86.

Hoan, P.V., Ha, Y., 2021, ARAS-FUCOM approach for VPAF fighter aircraft selection, Decision Science Letters, 10(1), pp. 53-62.

Brauers, W.K.M., Zavadskas, E.K., 2010, Project management by multimoora as an instrument for transition economies, Technological and Economic Development of Economy, 16(1), pp. 5-24.

Brauers, W.K.M., Zavadskas, E.K., 2011, Multimoora Optimization Used to Decide on a Bank Loan to Buy Property, Technological and Economic Development of Economy, 17(1), pp. 174-188.

Balezentis, T., Zeng, S., 2013, Group multi-criteria decision making based upon interval-valued fuzzy numbers: An extension of the MULTIMOORA method, Expert System with Applications, 40(2), pp. 543-550.

Aksoy, E., Ömürbek, N., Karaatlı, M., 2015, Use of AHP Based MULTIMOORA and COPRAS Methods for Evaluating the Performance of Turkish Coal Enterprises, Hacettepe University Journal of Economics and Administrative Sciences, 33(4), pp. 1-28.

Hafezalkotob, A., Hafezalkotob, A., Sayadi, M.K., 2016, Extension of MULTIMOORA Method with Interval Numbers: An Application in Materials Selection, Applied Mathematical Modelling, 40(2), pp.1372-1386.

Karabasevic, D., Stanujkic, D., Urosevic, S., Maksimovic, M., 2015, Selection of candidates in the mining industry based on the application of the SWARA and the MULTIMOORA methods, Acta Montanistica Slovaca, 20(2), pp. 116-124.

Zavadskas, E.K., Bausys, R., Juodagalviene, B., Sapranaviciene, I.G., 2017, Model for residential house element and material selection by neutrosophic MULTIMOORA method, Engineering Applications of Artificial Intelligence, 64, pp. 315-324.

Fattahi, R., Khalilzadeh, M., 2018, Risk evaluation using a novel hybrid method based on FMEA, extended MULTIMOORA, and AHP methods under fuzzy environment, Safety Science, 102, pp. 290-300.

Maghsoodi, A.I., Abouhamzeh, G., Khalilzadeh, M., Zavadskas, E.K., 2018, Ranking and selecting the best performance appraisal method using the MULTIMOORA approach integrated Shannon’s entropy, Frontiers of Business Research in China, 12(2), pp. 1-21.

Wang, W., Liu, X., Qin, Y., 2018, A fuzzy Fine-Kinney-based risk evaluation approach with extended MULTIMOORA method based on Choquet integral, Computers & Industrial Engineering, 125, pp. 111-123.

Zarch, M.E., Moghaddam, R.T., Esfahanian, F., Sepehri, M.M., Azaron, A., 2018, Pharmacological therapy selection of type 2 diabetes based on the SWARA and modified MULTIMOORA methods under a fuzzy environment, Artificial Intelligence in Medicine, 87, pp. 20-33.

Liang, W., Zhao, G., Hong, C., 2019, Selecting the optimal mining method with extended multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA) approach, Neural Computing and Applications, 31, pp. 5871-5886.

Dorfeshan, Y., Mousavi, S.M., Mohagheghi, V., Vahdani, B, 2018, Selecting project-critical path by a new interval type-2 fuzzy decision methodology based on MULTIMOORA, MOOSRA and TPOP methods, Computers & Industrial Engineering, 120, pp. 160-178.

Liao, H., Qin, R., Gao, C., Wu, X., Hafezalkotob, A., Herrera, F., 2019, Score-HeDLiSF: A score function of hesitant fuzzy linguistic term set based on hesitant degrees and linguistic scale functions: An application to unbalanced hesitant fuzzy linguistic MULTIMOORA, Information Fusion, 48, pp. 39-54.

Gündoğdu, F. K., 2020, A spherical fuzzy extension of MULTIMOORA method, Journal of Intelligent & Fuzzy Systems, 38(1), pp. 963-978.

Lin, M., Huang, C., Xu, Z. 2020, MULTIMOORA based MCDM model for site selection of car sharing station under picture fuzzy environment, Sustainable Cities and Society, 53, 101873.

Asante, D., He, Z., Adjei, N.O., Asante, B., 2020, Exploring the barriers to renewable energy adoption utilising MULTIMOORA- EDAS method, Energy Policy, 142, 111479.

Rahimi, S., Hafezalkotob, A., Monavari, S.M., Hafezalkotob, A., Rahimi, R., 2020, Sustainable landfill site selection for municipal solid waste based on a hybrid decision-making approach: Fuzzy group BWM-MULTIMOORA-GIS, Journal of Cleaner Production, 248, 119186.

Tavana, M., Shaabani, A., Mohammadabadi, S.M., Varzgani, N., 2021, An integrated fuzzy AHP- fuzzy MULTIMOORA model for supply chain risk-benefit assessment and supplier selection, International Journal of Systems Science: Operations & Logistics, 8(3), pp. 238-261.

Wu, S.M., You, X.Y., Liu, H.C., Wang, L.E., 2020, Improving quality function deployment analysis with the cloud MULTIMOORA method, International Transactions in Operational Research, 27(3), pp. 1600-1621.

Tanrıverdi, G., Lezki, Ş., 2021, Istanbul Airport (IGA) and quest of best competitive strategy for air cargo carriers in new competition environment: A fuzzy multi-criteria approach, Journal of Air Transport Management, 95, 102088.

Zadeh, L.A., 1965, Fuzzy sets, Information and control, 8(3), pp. 338-353.

Zadeh, L.A., 1975, The concept of a linguistic variable and its application to approximate reasoning-I, Information Sciences, 8(3), pp. 199-249.

Atanassov, K., 1986, Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20(1), pp. 87-96.

Yager, R.R., 2014, Pythagorean membership grades in multicriteria decision making, IEEE Transactions on Fuzzy Systems, 22(4), pp. 958–965.

Yager, R.R., 2017, Generalized orthopair fuzzy sets, IEEE Transactions on Fuzzy Systems, 25(5), pp. 1222–1230.

Si, A., Das, S., Kar, S., 2019, An approach to rank picture fuzzy numbers for decision making problems, Decision Making: Applications in Management and Engineering, 2(2), pp. 54-64.

Mishra, A.R., Rani, P., Pardasani, K.R., Mardani, A., Stević, Z., Pamučar, D., 2020, A novel entropy and divergence measures with multi-criteria service quality assessment using interval valued intuitionistic fuzzy TODIM method, Soft Computing, 24, pp. 11641-11661.

Mishra, A.R., Sisodia, G., Raj Pardasani, K., Sharma, K., 2020, Multi-criteria IT personnel selection on intuitionistic fuzzy information measures and ARAS methodology, IranianJournal of Fuzzy Systems, 17(4), pp. 55-68.

Smarandache, F., 2015, α-Discounting Method for Multi-Criteria Decision Making (α-D MCDM), SCS AdSumus, Oradea, Romania & Educational Publisher, Columbus, USA.

Pramanik, S., Biswas, P., Giri, B. C., 2017, Hybrid vector similarity measures and their applications to multi-attribute decision making under neutrosophic environment, Neural Computing and Applications, 28, pp. 1163–1176.

Liu, D., Liu, G., Liu, Z., 2018, Some Similarity Measures of Neutrosophic Sets Based on the Euclidean Distance and Their Application in Medical Diagnosis, Computational and Mathematical Methods in Medicine, 7325938.

Liu, F., Aiwu, G., Lukovac, V., Vukić, M., 2018, A multicriteria model for the selection of the transport service provider: a single valued neutrosophic DEMATEL multicriteria model, Decision Making: Applications in Management and Engineering, 1(2), pp. 121-130.

Wang, H., Smarandache, F., Zhang, Y.Q., Sunderraman, R., 2010, Single valued neutrosophic sets, Multispace and Multistructure, 4, pp. 410-413.

Pamučar, D., Božanić, D., 2019, Selection of a location for the development of multimodal logistics center: Application of single-valued neutrosophic MABAC model, Operational Research in Engineering Sciences: Theory and Applications, 2(2), pp. 55-71.

Rani, P., Mishra, A.R., 2020, Single-valued neutrosophic SWARA-VIKOR framework for performance assessment of eco-industrial thermal power plants, ICSES Transactions on Neural and Fuzzy Computing, 3(1), 335.

Sodenkamp, M.A., Tavana, M., Di Caprio, D., 2018, An aggregation method for solving group multi-criteria decision-making problems with single-valued neutrosophic sets, Applied Soft Computing, 71, pp. 715-727.

Thao, N.X., Smarandache, F., 2018, Divergence Measure of Neutrosophic Sets and Applications, Neutrosophic Sets and Systems, 21, pp. 142-152.

Qin, K., Wang, L., 2020, New similarity and entropy measures of single-valued neutrosophic sets with applications in multi-attribute decision making, Soft Computing, 24, pp. 16165-16176.

Tian, C., Peng, J.J., Zhang, Z.Q., Goh, M., Wang, J.Q., 2020, A Multi-Criteria Decision- Making Method Based on Single-Valued Neutrosophic Partitioned Heronian Mean Operator, Mathematics, 8(7), 1189.

Biswas, P., Pramanik, S., Giri, B.C., 2016, TOPSIS method for multi-attribute group decision making under single-valued neutrosophic environment, Neural Computing and Applications, 27, pp. 727-737.

Mondal, K., Pramanik, S., Smarandache, F., 2016, Several Trigonometric Hamming Similarity Measures of Rough Neutrosophic Sets and their Applications in Decision Making, In New Trends in Neutrosophic Theory and Applications, F. Smarandache and S. Pramanik (Ed.), Pons Publishing House, Brussels, pp. 93-103.

Biswas, P., Pramanik, S., Giri, B.C., 2016, Some Distance Measures of Single Valued Neutrosophic Hesitant Fuzzy Sets and Their Applications to Multiple Attribute Decision Making, In New Trends in Neutrosophic Theory and Applications, F. Smarandache and S. Pramanik (Ed.), Pons Publishing House, Brussels, pp. 27-34.

Abdel-Basset, M., Mohamed, M., Zhou, Y., Hezam, I., 2017, Multi-criteria group decision making based on neutrosophic analytic hierarchy process, Journal of Intelligent & Fuzzy Systems, 33(6), pp. 4055-4066.

Abdel-Basset, M., Mohamed, M., Smarandache, F., 2018, An extension of neutrosophic AHP-SWOT analysis for strategic planning and decision making, Symmetry, 10(4), 116.

Stanujkic, D., Zavadskas, E.K., Smarandache, F., Brauers, W.K.M., Karabasevic, D., 2017, A Neutrosophic Extension of the Multimoora Method, Informatica, 28(1), pp. 181-192.

Şahin, R., 2014, Multi-criteria neutrosophic decision making method based on score and accuracy functions under neutrosophic environment, arXiv:1412.5202.

Balezentis, A., Balezentis, T., 2011, An innovative multi criteria supplier selection based on two tuple MULTIMOORA and hybrid data,Economic Computation and Economic Cybernetics Studies and Research, 45, pp. 37-56.

Zavadskas, E.K., Antucheviciene, J., Saparauskas, J., Turskis, Z., 2013, MCDM methods WASPAS and MULTIMOORA: verification of robustness of methods when assessing alternative solutions, Economic Computation and Economic Cybernetics Studies and Research, 47(2), pp. 5-20.

Zeng, S., Balezentis, A., Su, W., 2013, The multi criteria hesitant fuzzy group decision making with MULTIMOORA method, Economic Computation and Economic Cybernetics Studies and Research, 47(3), pp. 171-184.

Liu, H.C., You, J.X., Lu, C., Chen, Y.Z., 2015, Evaluating healthcare waste treatment technologies using a hybrid multi criteria decision making model, Renewable and Sustainable Energy Reviews, 41, pp. 932-942.

Liu,H.C.,Fan, X.J., Li, P., Chen, Y.Z., 2014, Evaluating the risk of failure modes with extended MULTIMOORA method under fuzzy environment, Engineering Applications of Artificial Intelligence, 34, pp. 168-177.

Hafezalkotob, A., Hafezalkotob, A., 2017, Interval MULTIMOORA method with target values of attributes based on interval distance and preference degree:biomaterials selection,Journal of Industrial Engineering International, 13, pp. 181-198.

Zhao, H., You, J.X., Liu, H.C., 2017,Failure mode and effect analysis using MULTIMOORA method with continuous weighted entropy under interval-valued intuitionistic fuzzy environment, Soft Computing, 21, pp. 5355-5367.

Dai, W., Zhong, Q., Qi, C., 2020, Multi-stage multi-attribute decision-making method based on the prospect theory and triangular fuzzy MULTIMOORA, Soft Computing, 24, pp. 9429-9440.

Wensveen, J.G., 2011,Air transportation: a management perspective, 7th ed. Aldershot, England: Ashgate Publishing Ltd.

Ortega, R.G.,Vázquez, M.L., Figueiredo, J.A.S., Rodríguez, A.G., 2018, Sinos River basin Social-environmental prospective assessment of water quality management using fuzzy cognitive maps and neutrosophic AHP-TOPSIS, Neutrosophic Sets and Systems, 23, pp. 160-171.

Ye, J., 2014, Single-Valued Neutrosophic Minimum Spanning Tree and Its Clustering Method, Journal of Intelligent Systems, 23(3), pp. 311-324.




DOI: https://doi.org/10.22190/FUME210521059K

Refbacks

  • There are currently no refbacks.


ISSN: 0354-2025 (Print)

ISSN: 2335-0164 (Online)

COBISS.SR-ID 98732551

ZDB-ID: 2766459-4