Abstract
From a systematic review on the use of FCMs and their extensions, it is identified that there are shortcomings in the works reported in the consulted bibliography regarding the treatment of indeterminacy and the solution of multistage sequential decision-making problems. In this paper, two new extensions of Fuzzy Cognitive Maps (FCMs) for multistage sequential decision-making problems are proposed. The Multistage Sequential Triangular Neutrosophic Cognitive Map (MSTrNCM) combines neutrosophic theory with computer with words techniques to represent the map’s relationships and the inference process. This extension improves the modeling of indeterminacy and the interpretability of results. The second map, which is called Neutrosophic Cognitive Map based on linguistic Data Summarization (NCM-LDS), uses linguistic summaries to represent the map’s relations and to carry out the inference process. One of the main advantages of this extension is that it facilitates the maps construction and interpretability. Furthermore, the suggested extensions are applied as a decision-making support tool for projects evaluation using a dataset with 1011 projects records. In experimental analysis, the two proposed extensions MSTrNCM and NCM_LDS report better results than the traditional FCM and NCM_Indeterminacy reported in bibliography.
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References
Johnson, J.: CHAOS report: decision latency theory: it is all about the interval. The standish group (2018)
Pérez, I., García, R., Piñero, P.Y., Mahdi, G.S., Peña, M.: Experiencias en el uso de tecnicas de softcomputing en la evaluacion de proyectos de software. Investigación Oper. 41, 108–120 (2020)
Al-subhi, S.H., Papageorgiou, E.I., Pérez, P.P., Mahdi, G.S., Acuña, L.A.: Triangular neutrosophic cognitive map for multistage sequential decision-making problems. Int. J. Fuzzy Syst., pp. 1–23 doi: https://doi.org/10.1007/s40815-020-01014-5 (2021).
Dursun, M., Goker, N., Mutlu, H.: A cognitive map integrated intuitionistic fuzzy decision-making procedure for provider selection in project management. J. Intell. Fuzzy Syst 39, 6645–6655 (2020). https://doi.org/10.3233/JIFS-189125
Kosko, B.: Fuzzy cognitive maps. Int. J. Man Mach. Stud. 24, 65–75 (1986). https://doi.org/10.1016/S0020-7373(86)80040-2
Rickard, J.T., Aisbett, J., Yager, R.R.: Computing with words in fuzzy cognitive maps. Annual conference of the North American fuzzy information processing society (NAFIPS) held jointly with 5th world conference on soft computing (WConSC), pp. 1–6 (2015). doi: https://doi.org/10.1109/NAFIPS-WConSC.2015.7284135
Frías, M., Filiberto, Y., Nápoles, G., Vanhoof, K., Bello, R.: Fuzzy cognitive maps reasoning with words: the ordinal case. 2nd International Symposium on Fuzzy and Rough Sets, Cuba (2017)
González, M.P., De La Rosa, C.G., Moran, F.J.: Fuzzy cognitive maps and computing with words for modeling project portfolio risks interdependencies. Int. J. Innov. Appl. Stud. 15, 737–742 (2016)
Salmeron, J.L.: Modelling grey uncertainty with fuzzy grey cognitive maps. Expert Syst. Appl. 37, 7581–7588 (2010). https://doi.org/10.1016/j.eswa.2010.04.085
Kang, B., Deng, Y., Sadiq, R., Mahadevan, S.: Evidential cognitive maps. Knowl.-Based Syst. 35, 77–86 (2012). https://doi.org/10.1016/j.knosys.2012.04.007
Mkrtchyan, L., Ruan, D.: Belief degree-distributed fuzzy cognitive maps. IEEE Int. Conf. Intell. Syst. Knowl. Eng., pp. 159–165 (2010). doi: https://doi.org/10.1109/ISKE.2010.5680815
Jia, Z., Zhang, Y., Dong, X.: An extended intuitionistic fuzzy cognitive map via Dempster-Shafer theory. IEEE Access 8, 23186–23196 (2020). https://doi.org/10.1109/ACCESS.2020.2970159
Vasantha, W.B., Kandasamy, I., Devvrat, V., Ghildiyal, Sh.: Study of imaginative play in children using neutrosophic cognitive maps model. Neutrosophic Sets Syst. 30 (2019). doi: https://doi.org/10.5281/zenodo.3569702
Chithra, B., Nedunchezhian, R.: Dynamic neutrosophic cognitive map with improved cuckoo search algorithm (DNCM-ICSA) and ensemble classifier for rheumatoid arthritis (RA) disease. J. King Saud Univ. Comput. Inform. Sci. (2020). https://doi.org/10.1016/j.jksuci.2020.06.011
Smarandache, F.: A Unifying Field in Logics: Neutrosophic Logic, Neutrosophy, Neutrosophic Set, Neutrosophic Probability. American Research Press, Rehoboth N.M (1999)
Kandasamy, W.B., Smarandache, F.: Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps. Xiquan, New Mexico, USA (2003)
Amirkhani, A., Papageorgiou, E.I., Mohseni, A., Mosavi, M.R.: A review of fuzzy cognitive maps in medicine: Taxonomy, methods and applications. Comput. Methods Prog. Biomed 142, 129–145 (2017). https://doi.org/10.1016/j.cmpb.2017.02.021
Rezaee, J., Yousefi, M., Valipour, S., Dehdar, M.: Risk analysis of sequential processes in food industry integrating multi-stage fuzzy cognitive map and process failure mode and effects analysis. Comput. Ind. Eng. 123, 325–337 (2018). https://doi.org/10.1016/j.cie.2018.07.012
Martin, N., Aleeswari, A., Merline, W.: Risk factors of lifestyle diseases – analysis by decagonal linguistic neutrosophic fuzzy cognitive map. Mater. Today: Proc 24, 1939–1943 (2020). https://doi.org/10.1016/j.matpr.2020.03.621
Bhutani, K., Kumar, M., Garg, G., Aggarwal, S.: Assessing it projects success with extended fuzzy cognitive maps & neutrosophic cognitive maps in comparison to fuzzy cognitive maps. NSS 12, 9–19 (2016)
Liu, P., Wang, Y.: Multiple attribute decision-making method based on single-valued neutrosophic normalized weighted Bonferroni mean. Neural Comput. Appl. 25, 2001–2010 (2014). https://doi.org/10.1007/s00521-014-1688-8
Bueno, S., Salmeron, J.L.: Benchmarking main activation functions in fuzzy cognitive maps. Expert Syst. Appl. 36, 5221–5229 (2009). https://doi.org/10.1016/j.eswa.2008.06.072
Pérez, I., Piñero, P.Y., Bello, R., Acuña, L.A., García, R.: Linguistic Summaries Generation with Hybridization Method Based on Rough and Fuzzy Sets. Rough Sets, pp. 385–397. Springer International Publishing, Havana, Cuba. doi: https://doi.org/10.1007/978-3-030-52705-1_29 (2020)
Wang, H., Smarandache, F., Zhang, Y., Sunderraman, R.: Single valued neutrosophic sets. Multisp. Multistruct. 4, 410–413 (2010)
Piñero, P.Y., Pérez, I., Hechavarría, C.C., Rojas, C., González, R., Torres, S.: Repositorio de datos para investigaciones en gestión de proyectos. Revista Cubana de Ciencias Inform 13, 176–191 (2019)
Martínez, L., Rodriguez, R.M., Herrera, F.: The 2-tuple Linguistic Model: Computing with Words in Decision Making. Springer International Publishing, Switzerland, doi: https://doi.org/10.1007/978-3-319-24714-4 (2015).
Nápoles, G., Grau, I., Papageorgiou, E., Bello, R., Vanhoof, K.: rough cognitive networks. Knowl.-Based Syst. 91, 46–61 (2016). https://doi.org/10.1016/j.knosys.2015.10.015
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Al-subhi, S.S.H., Pérez Pupo, I., Piñero Pérez, P.Y., Mahdi, G.S.S., Villavicencio Bermúdez, N. (2022). New Extensions of Fuzzy Cognitive Maps for Sequential Multistage Decision-Making Problems: Application in Project Management. In: Piñero Pérez, P.Y., Bello Pérez, R.E., Kacprzyk, J. (eds) Artificial Intelligence in Project Management and Making Decisions. UCIENCIA 2021. Studies in Computational Intelligence, vol 1035. Springer, Cham. https://doi.org/10.1007/978-3-030-97269-1_10
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