An Interval-Valued Intuitionistic Fuzzy VIKOR Approach for R&D Project Selection in Defense Industry Investment Decisions
DOI:
https://doi.org/10.31181/jscda21202428Keywords:
Project Selection, MCDM, Interval Valued Intuitionistic Fuzzy Sets, VIKOR, Defense IndustryAbstract
Defense Industry projects are high-budget projects that take many years, required infrastructure, and go through serious research and development processes. When making investment decisions, solutions obtained with models based on expert opinions and analytical techniques are required. At this point, interval-valued intuitionistic fuzzy (IVIF) methods are a good alternative to eliminate uncertainty, especially in order to evaluate all preference information provided by decision makers. In this study, multi-criteria decision making in the IVIF environment in the defense industry is extended with the VIKOR method. For the application, the literature was examined and 8 different criteria were determined. 4 different alternative projects were evaluated based on these criteria by 3 different decision makers. The results show that the analyzes performed with the VIKOR method in the IVIF environment are similar to the literature. Additionally, there is no study in the literature examining this methodology on the defense industry.
References
Atanassov, K. T. (1994). New operations defined over the intuitionistic fuzzy sets. Fuzzy sets and Systems, 61(2), 137-142. https://doi.org/10.1016/0165-0114(94)90229-1
Atanassov, K. T., & Stoeva, S. (1986). Intuitionistic fuzzy sets. Fuzzy sets and Systems, 20(1), 87-96. https://doi.org/10.1016/S0165-0114(86)80034-3
Atanassov, K., & Gargov, G. (1998). Elements of intuitionistic fuzzy logic. Part I. Fuzzy sets and systems, 95(1), 39-52. https://doi.org/10.1016/S0165-0114(96)00326-0
Bahadori, M., Hosseini, S. M., Teymourzadeh, E., Ravangard, R., Raadabadi, M., & Alimohammadzadeh, K. (2020). A supplier selection model for hospitals using a combination of artificial neural network and fuzzy VIKOR. International Journal of Healthcare Management, 13(4), 286-294. https://doi.org/10.1080/20479700.2017.1404730
Bai, Y., & Wang, D. (2011, June). Evaluate and identify optimal weapon systems using fuzzy multiple criteria decision making. In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011) (pp. 1510-1515). IEEE. http://dx.doi.org/10.1109/FUZZY.2011.6007311
Büyüközkan, G., Göçer, F. (2019). Smart Medical Device Selection Based on Intuitionistic Fuzzy Choquet Integral, Soft Computing, 23, 10085-10103, 2019. https://doi.org/10.1007/s00500-018-3563-5
Büyüközkan, G., Göçer, F., & Feyzioğlu, O. (2018). Cloud computing technology selection based on interval-valued intuitionistic fuzzy MCDM methods. Soft Computing, 22(15), 5091-5114. https://doi.org/10.1007/s00500-018-3317-4
Chen, S. M. (1996). Evaluating weapon systems using fuzzy arithmetic operations. Fuzzy sets and systems, 77(3), 265-276. http://dx.doi.org/10.1016/0165-0114(95)00096-8
Cheng, C. H. (1997). Evaluating naval tactical missile systems by fuzzy AHP based on the grade value of membership function. European journal of operational research, 96(2), 343-350. http://dx.doi.org/10.1016/S0377-2217(96)00026-4
Cheng, C. H. (1999). Evaluating weapon systems using ranking fuzzy numbers. Fuzzy sets and systems, 107(1), 25-35. http://dx.doi.org/10.1016/S0165-0114(97)00348-5
Costa, I. P. D. A., Costa, A. P. D. A., Sanseverino, A. M., Gomes, C. F. S., & Santos, M. D. (2022). Bibliometric studies on multi-criteria decision analysis (MCDA) methods applied in military problems. Pesquisa Operacional, 42, e249414. http://dx.doi.org/10.1590/0101-7438.2022.042.00249414
Dammak, F., Baccour, L., & Alimi, A. M. (2020). A new ranking method for TOPSIS and VIKOR under interval valued intuitionistic fuzzy sets and possibility measures. Journal of Intelligent & Fuzzy Systems, 38(4), 4459-4469. https://doi.org/10.3233/JIFS-191223
de Almeida, I. D. P., Corriça, J. V. D. P., Costa, A. P. D. A., Costa, I. P. D. A., Maêda, S. M. D. N., Gomes, C. F. S., & dos Santos, M. (2020, December). Study of the location of a second fleet for the Brazilian Navy: Structuring and Mathematical Modeling Using SAPEVO-M and VIKOR Methods. In International Conference of Production Research-Americas (pp. 113-124). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-76310-7_9
Gul, M., Celik, E., Aydin, N., Gumus, A. T., & Guneri, A. F. (2016). A state of the art literature review of VIKOR and its fuzzy extensions on applications. Applied soft computing, 46, 60-89. https://doi.org/10.1016/j.asoc.2016.04.040
Karadayi, M. A., Ekinci, Y., & Tozan, H. (2019). A fuzzy MCDM framework for weapon systems selection. In Operations Research for Military Organizations (pp. 185-204). IGI Global. http://dx.doi.org/10.4018/978-1-5225-5513-1.ch009
Kokoç, M., & Ersöz, S. (2021). A literature review of interval-valued intuitionistic fuzzy multi-criteria decision-making methodologies. Operations Research and Decisions, 31(4). https://doi.org/10.37190/ord210405
Kurtay, K. G., Gökmen, Y., Altundaş, A., & Dağıstanlı, H. A. (2021). Savunma Sanayii Projelerinin Çok Kriterli Karar Verme Yöntemleriyle Önceliklendirilmesi Ve Karşilaştirilmasi: Karma Bir Model Önerisi. SAVSAD Savunma ve Savaş Araştırmaları Dergisi, 31(1), 1-24. Retrieved from https://dergipark.org.tr/en/pub/savsad/issue/63016/957426
Lee, J. G., & Park, M. J. (2020). Evaluation of technological competence and operations efficiency in the defense industry: The strategic planning of South Korea. Evaluation and program planning, 79, 101775. https://doi.org/10.1016/j.evalprogplan.2019.101775
Li, C., & Jiang, H. (2011, August). Extension of VIKOR method with interval-valued intuitionistic fuzzy sets. In 2011 International Conference on Management and Service Science (pp. 1-4). IEEE https://doi.org/10.1109/ICMSS.2011.5999210
Liu, D. Y., Wang, C. K., Fang, C. Y., & Liu, P. L. (2021). A Study of Project Financing on the Defense Industry in Systems Thinking Perspective. Journal of Applied Finance and Banking, 11(2), 131-149. https://doi.org/10.47260/jafb/1126
Liu, P., & Qin, X. (2017). An extended VIKOR method for decision making problem with interval-valued linguistic intuitionistic fuzzy numbers based on entropy. Informatica, 28(4), 665-685. https://doi.org/10.15388/Informatica.2017.151
Lu, S., Zhou, J., & Ren, J. (2023). Alleviating Energy Poverty through Renewable Energy Technology: An Investigation Using a Best-Worst Method-Based Quality Function Deployment Approach with Interval-Valued Intuitionistic Fuzzy Numbers. International Journal of Energy Research, 2023. https://doi.org/10.1155/2023/8358799
Mon, D. L., Cheng, C. H., & Lin, J. C. (1994). Evaluating weapon system using fuzzy analytic hierarchy process based on entropy weight. Fuzzy sets and systems, 62(2), 127-134. http://dx.doi.org/10.1016/0165-0114(94)90052-3
Narayanamoorthy, S., Geetha, S., Rakkiyappan, R., & Joo, Y. H. (2019). Interval-valued intuitionistic hesitant fuzzy entropy based VIKOR method for industrial robots selection. Expert Systems with Applications, 121, 28-37. https://doi.org/10.1016/j.eswa.2018.12.015
Norheim-Martinsen, P. M., & Nyhamar, T. (Eds.). (2015). International military operations in the 21st century: Global trends and the future of intervention. Routledge. https://doi.org/10.4324/9781315744773
Opricovic, S., & Tzeng, G. H. (2002). Multicriteria planning of post‐earthquake sustainable reconstruction. Computer‐Aided Civil and Infrastructure Engineering, 17(3), 211-220. https://doi.org/10.1111/1467-8667.00269
Öztürk, C., Yildizbasi, A., Yilmaz, I., & Ariöz, Y. (2021, August). Vaccine Selection Using Interval-Valued Intuitionistic Fuzzy VIKOR: A Case Study of COVID-19 Pandemic. In International Conference on Intelligent and Fuzzy Systems (pp. 101-108). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-85577-2_12
Park, J. H., Cho, H. J., & Kwun, Y. C. (2011). Extension of the VIKOR method for group decision making with interval-valued intuitionistic fuzzy information. Fuzzy Optimization and Decision Making, 10, 233-253. https://doi.org/10.1007/s10700-011-9102-9
Qi, W., Huang, Z., Dinçer, H., Korsakienė, R., & Yüksel, S. (2020). Corporate governance-based strategic approach to sustainability in energy industry of emerging economies with a novel interval-valued intuitionistic fuzzy hybrid decision making model. Sustainability, 12(8), 3307. https://doi.org/10.3390/su12083307
Rahmi, B. A. K. İ. (2022). Extended VIKOR method based on interval-valued intuitionistic fuzzy numbers for selection of logistics centre location. İnsan ve Toplum Bilimleri Araştırmaları Dergisi, 11(3), 1821-1837. https://doi.org/10.15869/itobiad.1084212
Salimian, S., & Mousavi, S. M. (2023). A multi-criteria decision-making model with interval-valued intuitionistic fuzzy sets for evaluating digital technology strategies in COVID-19 pandemic under uncertainty. Arabian Journal for Science and Engineering, 48(5), 7005-7017. https://doi.org/10.1007/s13369-022-07168-8
Salimian, S., Mousavi, S. M., & Antucheviciene, J. (2022). An interval-valued intuitionistic fuzzy model based on extended VIKOR and MARCOS for sustainable supplier selection in organ transplantation networks for healthcare devices. Sustainability, 14(7), 3795. https://doi.org/10.3390/su14073795
Sennaroglu, B., & Celebi, G. V. (2018). A military airport location selection by AHP integrated PROMETHEE and VIKOR methods. Transportation Research Part D: Transport and Environment, 59, 160-173. https://doi.org/10.1016/j.trd.2017.12.022
Tan, C., & Chen, X. (2013). Interval-valued intuitionistic fuzzy multicriteria group decision making based on VIKOR and Choquet integral. Journal of Applied Mathematics, 2013 https://doi.org/10.1155/2013/656879
Tian, Z., & Zhang, S. (2021). Application of multi-attribute group decision-making methods in urban road traffic safety evaluation with interval-valued intuitionistic fuzzy information. Journal of Intelligent & Fuzzy Systems, 40(3), 5337-5346. https://doi.org/10.3233/JIFS-202142
Wang, P., Meng, P., & Song, B. (2014). Response surface method using grey relational analysis for decision making in weapon system selection. Journal of systems engineering and electronics, 25(2), 265-272. http://dx.doi.org/10.1109/JSEE.2014.00030
Wei, G., & Wang, X. (2007, December). Some geometric aggregation operators based on interval-valued intuitionistic fuzzy sets and their application to group decision making. In 2007 international conference on computational intelligence and security (CIS 2007) (pp. 495-499). IEEE. https://doi.org/10.1109/CIS.2007.84
Wu, L., Gao, H., & Wei, C. (2019). VIKOR method for financing risk assessment of rural tourism projects under interval-valued intuitionistic fuzzy environment. Journal of Intelligent & Fuzzy Systems, 37(2), 2001-2008 https://doi.org/10.3233/JIFS-179262
Xu, Z., & Chen, J. (2007, August). On geometric aggregation over interval-valued intuitionistic fuzzy information. In Fourth international conference on fuzzy systems and knowledge discovery (FSKD 2007) (Vol. 2, pp. 466-471). IEEE. https://doi.org/10.1109/FSKD.2007.427
Yang, S., Wang, S., Xu, X., & Li, G. (2014, August). A hybrid multiple attribute decision-making approach for evaluating weapon systems under fuzzy environment. In 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) (pp. 204-210). IEEE. http://dx.doi.org/10.1109/FSKD. 2014.6980833
Zhang, X., Jiang, J., Ge, B., & Yang, K. (2016, April). Group decision making for weapon systems selection with VIKOR based on consistency analysis. In 2016 Annual IEEE Systems Conference (SysCon) (pp. 1-6). IEEE. http://dx.doi.org/10.1109/SYSCON.2016.7490525
Zhao, X., Tang, S., Yang, S., & Huang, K. (2013). Extended VIKOR method based on cross-entropy for interval-valued intuitionistic fuzzy multiple criteria group decision making. Journal of Intelligent & Fuzzy Systems, 25(4), 1053-1066. https://doi.org/10.3233/IFS-130790
Zhao, X., Yang, S., & Yang, M. (2014). Extended VIKOR method with fuzzy cross-entropy of interval-valued intuitionistic fuzzy sets. In 2012 2nd International Conference on Computer and Information Application (ICCIA 2012) (pp. 1093-1096). Atlantis Press. https://doi.org/10.2991/iccia.2012.268
Zimmermann, H. J. (1987). Fuzzy sets, decision making, and expert systems (Vol. 10). Springer Science & Business Media. https://doi.org/10.1007/978-94-009-3249-4
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