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Kanıtsal Bulanık Çok Kriterli Karar Vermeyi Kullanarak Keskin Nişancı Tüfeği Seçimi

Year 2024, Volume: 34 Issue: 1, 137 - 152, 28.06.2024
https://doi.org/10.54078/savsad.1406855

Abstract

Karar verme süreçlerinde etkili olan belirsizliklerin temel nedenlerinden bazıları kesin olmama, rastgelelik ve muğlaklıktır. Bu belirsizliklerle başa çıkma yöntemlerinden biri de DST yöntemidir. DST, uygulamalarda özellikle hem rastgele ve eksik bilgi hem de tutarsızlık ile baş edebilme yeteneği ile öne çıkmaktadır. Bu çalışmanın temel amacı daha önce yapılmış bir keskin nişancı tüfeği seçim probleminde elde edilen sonuçlarla, EFMCDM kullanılarak DST yöntemiyle elde edilen sonuçları karşılaştırmak ve iki yöntemle elde edilen sonuçların birbiriyle uyumlu olup olmadığını değerlendirmektir. Çalışmada 4 keskin nişancı tüfeği 6 kritere göre değerlendirilmiştir. Araştırma bulgularından, DST yönteminin keskin nişancı tüfeği seçim problemi için üstünlük esaslı bulanık çok ölçütlü karar verme yöntemine benzer sonuçlar verdiği sonucuna varılmıştır. Ayrıca sonuçlar, güvenlik güçlerinin bu tür bir seçim problemi için DST yöntemini kullanabileceğini göstermektedir. Sonuç olarak, inanç entropisi yöntemine dayalı EFMCDM yönteminin benzer birçok seçim probleminde kullanılabileceği ortaya konmuştur.

References

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  • Arslan, G., & Aydın, Ö. (2009). A new software development for fuzzy multicriteria decision‐making. Technological and Economic Development of Economy, 15(2), 197-212. https://doi.org/10.3846/1392-8619.2009.15.197-212
  • Aygün, H., & Adalı, E. (2006). Dempster-Shafer algoritmasının kullanımı ile sınıflandırma algoritmalarının birleştirilmesi. İTÜDERGİSİ, 5(4). http://www.itudergi.itu.edu.tr/index.php/itudergisi_d/article/viewFile/462/401
  • Beynon, M., Curry, B., & Morgan, P. (2000). The Dempster–Shafer theory of evidence: an alternative approach to multicriteria decision modelling. Omega, 28(1), 37-50. https://doi.org/10.1016/S0305-0483(99)00033-X
  • Bozkaya, N., & Arslan, G. (2008). Üstünlük esaslı bulanık çok ölçütlü karar verme yönteminin keskin nişancı tüfeği seçimi problemine uygulaması. Savunma Bilimleri Dergisi, 7(1), 40-54. https://dergipark.org.tr/en/pub/khosbd/issue/19232/204362
  • Büyükyazıcı, M., & Sucu, M. (2009). Matematiksel kanıt kuramı'nda uzlaşma üretici yöntemler için bir çerçeve. İstatistikçiler Dergisi: İstatistik ve Aktüerya, 2(1), 19-27. https://dergipark.org.tr/en/pub/jssa/issue/10040/123863
  • Chatterjee, M., & Namin, A. S. (2021). A fuzzy Dempster–Shafer classifier for detecting web spams. Journal of Information Security and Applications, 59. https://doi.org/10.1016/j.jisa.2021.102793
  • Chinnasamy, S., Ramachandran, M., & Kurinjimalar Ramu, P. A. (2022). Study on fuzzy ELECTRE method with various methodologies. REST Journal on Emerging trends in Modelling and Manufacturing, 7(4), 108-115. https://doi.org/10.46632/7/4/2
  • Çavdur, F. (2005). Arama motorları kullanıcı oturumlarındaki konu değişikliklerinin tespit ve tahmin yöntemleri (Publication No. 198634)[MsC. Dissertation, Uludağ University]. YÖK National Thesis Center.
  • Danaee, P., Ghaeini, R., & Hendrix, D. A. (2017). A deep learning approach for cancer detection and relevant gene identification. In Pacific symposium on biocomputing 2017 (pp. 219-229). https://doi.org/10.1142/9789813207813_0022
  • Denœux, T., Dubois, D., & Prade, H. (2020). Representations of uncertainty in artificial intelligence: probability and possibility. In Marquis, P., Papini, O., Prade, H. (Eds.), A Guided Tour of Artificial Intelligence Research (pp.69-117). Springer. https://doi.org/10.1007/978-3-030-06164-7_3
  • Dutta, P., & Shome, S. (2023). A new belief entropy measure in the weighted combination rule under DST with faulty diagnosis and real-life medical application. International Journal of Machine Learning and Cybernetics, 14(4), 1179-1203. https://doi.org/10.1007/s13042-022-01693-6
  • Dymova, L., Kaczmarek, K., Sevastjanov, P., Sułkowski, Ł., & Przybyszewski, K. (2021). An approach to generalization of the intuitionistic fuzzy TOPSIS method in the framework of evidence theory. Journal of Artificial Intelligence and Soft Computing Research, 11(2), 157-175. https://doi.org/10.2478/jaiscr-2021-0010
  • Fei, L., & Feng, Y. (2021). Intuitionistic fuzzy decision‐making in the framework of Dempster–Shafer structures. International Journal of Intelligent Systems, 36(10). https://doi.org/10.1002/int.22517 Fei, L., & Ma, Y. (2023). A hybrid decision-making framework for selecting the emergency alternatives. International Journal of Fuzzy Systems, 1-15. https://doi.org/10.1007/s40815-023-01467-4
  • Fei, L., Xia, J., Feng, Y., & Liu, L. (2019). An ELECTRE-based multiple criteria decision making method for supplier selection using Dempster-Shafer theory. IEEE Access, 7. https://doi.org/10.1109/ACCESS.2019.2924945
  • Kang, B., & Deng, Y. (2019). The maximum Deng entropy. IEEE Access, 7. https://doi.org/10.1109/ACCESS.2019.2937679
  • Lin, K. P., & Hung, K. C. (2011). An efficient fuzzy weighted average algorithm for the military UAV selecting under group decision-making. Knowledge-Based Systems, 24(6), 877-889. https://doi.org/10.1016/j.knosys.2011.04.002
  • Liu, P., & Gao, H. (2019). Some intuitionistic fuzzy power Bonferroni mean operators in the framework of Dempster–Shafer theory and their application to multicriteria decision making. Applied Soft Computing, 85, 105790. https://doi.org/10.1016/j.asoc.2019.105790
  • Mokarram, M., & Sathyamoorthy, D. (2023). Determination of suitable locations for the construction of gas power plant using multicriteria decision and Dempster–Shafer model in GIS. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 45(1), 2846-2861. https://doi.org/10.1080/15567036.2019.1666189
  • Ngo, N. D. K., Tansuchat, R., Cu, P. V., Mau, T. N., Kohda, Y., & Huynh, V. N. (2023). A customer-driven evaluation method for service innovation in banking. IEEE Access, 23419242. https://doi.org/10.1109/ACCESS.2023.3292123
  • Qin, Y., Qi, Q., Shi, P., Scott, P. J., & Jiang, X. (2020). Novel operational laws and power Muirhead mean operators of picture fuzzy values in the framework of Dempster-Shafer theory for multiple criteria decision making. Computers & Industrial Engineering, 149. https://doi.org/10.1016/j.cie.2020.106853
  • Qin, Y., Qi, Q., Shi, P., Scott, P. J., & Jiang, X. (2023). A novel weighted averaging operator of linguistic interval-valued intuitionistic fuzzy numbers for cognitively inspired decision-making. Cognitive Computation, 1-19. https://doi.org/10.1007/s12559-023-10167-y
  • Rashki, M., & Faes, M. G. (2023). No-free-lunch theorems for reliability analysis. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 9(3), 04023019. https://doi.org/10.1061/AJRUA6.RUENG-1015
  • Ross, T. J. (2009). Fuzzy logic with engineering applications. John Wiley & Sons.
  • Seçkin, F. (2015). A Model development to formulate buyer-supplier integration levels and evalution criteria for sustainable supply chain management [Unpublished doctoral dissertation]. National Defense University, Turkish Air Force Academy.
  • Si, A., Das, S., & Kar, S. (2021). Picture fuzzy set-based decision-making approach using Dempster-Shafer theory of evidence and grey relation analysis and its application in COVID-19 medicine selection. Soft Computing, 1-15. https://doi.org/10.1007/s00500-021-05909-9
  • Sun, C., Li, S., & Deng, Y. (2020). Determining weights in multi-criteria decision making based on negation of probability distribution under uncertain environment. Mathematics, 8(2), 191. https://doi.org/10.3390/math8020191
  • Taban, C. (2019). UAV hub selection with fuzzy multi criteria decision making techniqoues for ensuring maritime safety [Unpublished MsC. dissertation]. Sakarya University.
  • Tang, X., Gu, X., Rao, L., & Lu, J. (2021). A single fault detection method of gearbox based on random forest hybrid classifier and improved Dempster-Shafer information fusion. Computers & Electrical Engineering, 92. https://doi.org/10.1016/j.compeleceng.2021.107101
  • Tong, Z., Xu, P., & Denoeux, T. (2021). An evidential classifier based on Dempster-Shafer theory and deep learning. Neurocomputing, 450, 275-293. https://doi.org/10.1016/j.neucom.2021.03.066
  • Turhan, H. İ. (2014). Decision making in tracking applications by using Dempster-Shafer Theory (Publication No. 384969) [MsC. Dissertation, Middle East Technical University]. YÖK National Thesis Center. https://open.metu.edu.tr/handle/11511/23984
  • Wu, D., & Tang, Y. (2020). An improved failure mode and effects analysis method based on uncertainty measure in the evidence theory. Quality and Reliability Engineering International, 36(5), 1786-1807. https://doi.org/10.1002/qre.2660
  • Wu, L., Tang, Y., Zhang, L., & Huang, Y. (2023). Uncertainty management in assessment of FMEA expert based on negation information and belief entropy. Entropy, 25(5). https://doi.org/10.3390/e25050800
  • Xiao, F. (2019). EFMCDM: Evidential fuzzy multicriteria decision making based on belief entropy. IEEE Transactions on Fuzzy Systems, 28(7), 1477-1491. https://doi.org/10.1109/TFUZZ.2019.2936368
  • Xiong, L., Su, X., & Qian, H. (2021). Conflicting evidence combination from the perspective of networks. Information Sciences, 580, 408-418. https://doi.org/10.1016/j.ins.2021.08.088
  • Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X
  • Zhang, Y., Dai, Y., & Liu, B. (2023). Identifying qualified public safety education venues using the Dempster–Shafer theory-based PROMETHEE method under linguistic environments. Mathematics, 11(4), 1011. https://doi.org/10.3390/math11041011
  • Zhong, Y., Zhang, H., Cao, L., Li, Y., Qin, Y., & Luo, X. (2023). Power muirhead mean operators of interval-valued intuitionistic fuzzy values in the framework of Dempster–Shafer theory for multiple criteria decision-making. Soft Computing, 27(2), 763-782. https://doi.org/10.1007/s00500-022-07595-7
  • Zhu, C., Qin, B., Xiao, F., Cao, Z., & Pandey, H. M. (2021). A fuzzy preference-based Dempster-Shafer evidence theory for decision fusion. Information Sciences, 570, 306-322. https://doi.org/10.1016/j.ins.2021.04.059
  • Zhu, C., & Xiao, F. (2021). A belief Hellinger distance for D–S evidence theory and its application in pattern recognition. Engineering Applications of Artificial Intelligence, 106, 104452. https://doi.org/10.1016/j.engappai.2021.104452

Sniper Rifle Selection Using Evidential Fuzzy Multi-Criteria Decision Making

Year 2024, Volume: 34 Issue: 1, 137 - 152, 28.06.2024
https://doi.org/10.54078/savsad.1406855

Abstract

Some of the main reasons for the uncertainties that are effective in the decision-making processes are imprecision, randomness, and ambiguity. One of the methods to deal with these uncertainties is the DST method. DST stands out in applications, especially with its ability to cope with both random and incomplete information and inconsistency. The main purpose of this study is to compare the results obtained in a previous sniper rifle selection problem with the results obtained by the DST method using EFMCDM and to evaluate whether the results obtained by the two methods are compatible with each other. In this study 4 sniper rifles were evaluated with respect to 6 criteria. From the research findings it was concluded that the DST method provides similar results to the outranking based fuzzy decision-making method for the sniper rifle selection problem. In addition, the results show that the security forces can use the DST method for this type of selection problem. In conclusion, it has been demonstrated that the EFMCDM method based on the belief entropy method can be used in many similar selection problems.

References

  • Aouam, T., Chang, S. I., & Lee, E. S. (2003). Fuzzy MADM: An outranking method. European Journal of Operational Research, 145(2), 317-328. https://doi.org/10.1016/S0377-2217(02)00537-4
  • Arslan, G., & Aydın, Ö. (2009). A new software development for fuzzy multicriteria decision‐making. Technological and Economic Development of Economy, 15(2), 197-212. https://doi.org/10.3846/1392-8619.2009.15.197-212
  • Aygün, H., & Adalı, E. (2006). Dempster-Shafer algoritmasının kullanımı ile sınıflandırma algoritmalarının birleştirilmesi. İTÜDERGİSİ, 5(4). http://www.itudergi.itu.edu.tr/index.php/itudergisi_d/article/viewFile/462/401
  • Beynon, M., Curry, B., & Morgan, P. (2000). The Dempster–Shafer theory of evidence: an alternative approach to multicriteria decision modelling. Omega, 28(1), 37-50. https://doi.org/10.1016/S0305-0483(99)00033-X
  • Bozkaya, N., & Arslan, G. (2008). Üstünlük esaslı bulanık çok ölçütlü karar verme yönteminin keskin nişancı tüfeği seçimi problemine uygulaması. Savunma Bilimleri Dergisi, 7(1), 40-54. https://dergipark.org.tr/en/pub/khosbd/issue/19232/204362
  • Büyükyazıcı, M., & Sucu, M. (2009). Matematiksel kanıt kuramı'nda uzlaşma üretici yöntemler için bir çerçeve. İstatistikçiler Dergisi: İstatistik ve Aktüerya, 2(1), 19-27. https://dergipark.org.tr/en/pub/jssa/issue/10040/123863
  • Chatterjee, M., & Namin, A. S. (2021). A fuzzy Dempster–Shafer classifier for detecting web spams. Journal of Information Security and Applications, 59. https://doi.org/10.1016/j.jisa.2021.102793
  • Chinnasamy, S., Ramachandran, M., & Kurinjimalar Ramu, P. A. (2022). Study on fuzzy ELECTRE method with various methodologies. REST Journal on Emerging trends in Modelling and Manufacturing, 7(4), 108-115. https://doi.org/10.46632/7/4/2
  • Çavdur, F. (2005). Arama motorları kullanıcı oturumlarındaki konu değişikliklerinin tespit ve tahmin yöntemleri (Publication No. 198634)[MsC. Dissertation, Uludağ University]. YÖK National Thesis Center.
  • Danaee, P., Ghaeini, R., & Hendrix, D. A. (2017). A deep learning approach for cancer detection and relevant gene identification. In Pacific symposium on biocomputing 2017 (pp. 219-229). https://doi.org/10.1142/9789813207813_0022
  • Denœux, T., Dubois, D., & Prade, H. (2020). Representations of uncertainty in artificial intelligence: probability and possibility. In Marquis, P., Papini, O., Prade, H. (Eds.), A Guided Tour of Artificial Intelligence Research (pp.69-117). Springer. https://doi.org/10.1007/978-3-030-06164-7_3
  • Dutta, P., & Shome, S. (2023). A new belief entropy measure in the weighted combination rule under DST with faulty diagnosis and real-life medical application. International Journal of Machine Learning and Cybernetics, 14(4), 1179-1203. https://doi.org/10.1007/s13042-022-01693-6
  • Dymova, L., Kaczmarek, K., Sevastjanov, P., Sułkowski, Ł., & Przybyszewski, K. (2021). An approach to generalization of the intuitionistic fuzzy TOPSIS method in the framework of evidence theory. Journal of Artificial Intelligence and Soft Computing Research, 11(2), 157-175. https://doi.org/10.2478/jaiscr-2021-0010
  • Fei, L., & Feng, Y. (2021). Intuitionistic fuzzy decision‐making in the framework of Dempster–Shafer structures. International Journal of Intelligent Systems, 36(10). https://doi.org/10.1002/int.22517 Fei, L., & Ma, Y. (2023). A hybrid decision-making framework for selecting the emergency alternatives. International Journal of Fuzzy Systems, 1-15. https://doi.org/10.1007/s40815-023-01467-4
  • Fei, L., Xia, J., Feng, Y., & Liu, L. (2019). An ELECTRE-based multiple criteria decision making method for supplier selection using Dempster-Shafer theory. IEEE Access, 7. https://doi.org/10.1109/ACCESS.2019.2924945
  • Kang, B., & Deng, Y. (2019). The maximum Deng entropy. IEEE Access, 7. https://doi.org/10.1109/ACCESS.2019.2937679
  • Lin, K. P., & Hung, K. C. (2011). An efficient fuzzy weighted average algorithm for the military UAV selecting under group decision-making. Knowledge-Based Systems, 24(6), 877-889. https://doi.org/10.1016/j.knosys.2011.04.002
  • Liu, P., & Gao, H. (2019). Some intuitionistic fuzzy power Bonferroni mean operators in the framework of Dempster–Shafer theory and their application to multicriteria decision making. Applied Soft Computing, 85, 105790. https://doi.org/10.1016/j.asoc.2019.105790
  • Mokarram, M., & Sathyamoorthy, D. (2023). Determination of suitable locations for the construction of gas power plant using multicriteria decision and Dempster–Shafer model in GIS. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 45(1), 2846-2861. https://doi.org/10.1080/15567036.2019.1666189
  • Ngo, N. D. K., Tansuchat, R., Cu, P. V., Mau, T. N., Kohda, Y., & Huynh, V. N. (2023). A customer-driven evaluation method for service innovation in banking. IEEE Access, 23419242. https://doi.org/10.1109/ACCESS.2023.3292123
  • Qin, Y., Qi, Q., Shi, P., Scott, P. J., & Jiang, X. (2020). Novel operational laws and power Muirhead mean operators of picture fuzzy values in the framework of Dempster-Shafer theory for multiple criteria decision making. Computers & Industrial Engineering, 149. https://doi.org/10.1016/j.cie.2020.106853
  • Qin, Y., Qi, Q., Shi, P., Scott, P. J., & Jiang, X. (2023). A novel weighted averaging operator of linguistic interval-valued intuitionistic fuzzy numbers for cognitively inspired decision-making. Cognitive Computation, 1-19. https://doi.org/10.1007/s12559-023-10167-y
  • Rashki, M., & Faes, M. G. (2023). No-free-lunch theorems for reliability analysis. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 9(3), 04023019. https://doi.org/10.1061/AJRUA6.RUENG-1015
  • Ross, T. J. (2009). Fuzzy logic with engineering applications. John Wiley & Sons.
  • Seçkin, F. (2015). A Model development to formulate buyer-supplier integration levels and evalution criteria for sustainable supply chain management [Unpublished doctoral dissertation]. National Defense University, Turkish Air Force Academy.
  • Si, A., Das, S., & Kar, S. (2021). Picture fuzzy set-based decision-making approach using Dempster-Shafer theory of evidence and grey relation analysis and its application in COVID-19 medicine selection. Soft Computing, 1-15. https://doi.org/10.1007/s00500-021-05909-9
  • Sun, C., Li, S., & Deng, Y. (2020). Determining weights in multi-criteria decision making based on negation of probability distribution under uncertain environment. Mathematics, 8(2), 191. https://doi.org/10.3390/math8020191
  • Taban, C. (2019). UAV hub selection with fuzzy multi criteria decision making techniqoues for ensuring maritime safety [Unpublished MsC. dissertation]. Sakarya University.
  • Tang, X., Gu, X., Rao, L., & Lu, J. (2021). A single fault detection method of gearbox based on random forest hybrid classifier and improved Dempster-Shafer information fusion. Computers & Electrical Engineering, 92. https://doi.org/10.1016/j.compeleceng.2021.107101
  • Tong, Z., Xu, P., & Denoeux, T. (2021). An evidential classifier based on Dempster-Shafer theory and deep learning. Neurocomputing, 450, 275-293. https://doi.org/10.1016/j.neucom.2021.03.066
  • Turhan, H. İ. (2014). Decision making in tracking applications by using Dempster-Shafer Theory (Publication No. 384969) [MsC. Dissertation, Middle East Technical University]. YÖK National Thesis Center. https://open.metu.edu.tr/handle/11511/23984
  • Wu, D., & Tang, Y. (2020). An improved failure mode and effects analysis method based on uncertainty measure in the evidence theory. Quality and Reliability Engineering International, 36(5), 1786-1807. https://doi.org/10.1002/qre.2660
  • Wu, L., Tang, Y., Zhang, L., & Huang, Y. (2023). Uncertainty management in assessment of FMEA expert based on negation information and belief entropy. Entropy, 25(5). https://doi.org/10.3390/e25050800
  • Xiao, F. (2019). EFMCDM: Evidential fuzzy multicriteria decision making based on belief entropy. IEEE Transactions on Fuzzy Systems, 28(7), 1477-1491. https://doi.org/10.1109/TFUZZ.2019.2936368
  • Xiong, L., Su, X., & Qian, H. (2021). Conflicting evidence combination from the perspective of networks. Information Sciences, 580, 408-418. https://doi.org/10.1016/j.ins.2021.08.088
  • Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X
  • Zhang, Y., Dai, Y., & Liu, B. (2023). Identifying qualified public safety education venues using the Dempster–Shafer theory-based PROMETHEE method under linguistic environments. Mathematics, 11(4), 1011. https://doi.org/10.3390/math11041011
  • Zhong, Y., Zhang, H., Cao, L., Li, Y., Qin, Y., & Luo, X. (2023). Power muirhead mean operators of interval-valued intuitionistic fuzzy values in the framework of Dempster–Shafer theory for multiple criteria decision-making. Soft Computing, 27(2), 763-782. https://doi.org/10.1007/s00500-022-07595-7
  • Zhu, C., Qin, B., Xiao, F., Cao, Z., & Pandey, H. M. (2021). A fuzzy preference-based Dempster-Shafer evidence theory for decision fusion. Information Sciences, 570, 306-322. https://doi.org/10.1016/j.ins.2021.04.059
  • Zhu, C., & Xiao, F. (2021). A belief Hellinger distance for D–S evidence theory and its application in pattern recognition. Engineering Applications of Artificial Intelligence, 106, 104452. https://doi.org/10.1016/j.engappai.2021.104452
There are 40 citations in total.

Details

Primary Language English
Subjects Defence Studies
Journal Section The Journal of Defence and War Studies June 2024
Authors

Galip Cihan Yalçın 0000-0001-9348-0709

Güvenç Arslan 0000-0002-4770-2689

Publication Date June 28, 2024
Submission Date December 19, 2023
Acceptance Date May 17, 2024
Published in Issue Year 2024 Volume: 34 Issue: 1

Cite

APA Yalçın, G. C., & Arslan, G. (2024). Sniper Rifle Selection Using Evidential Fuzzy Multi-Criteria Decision Making. SAVSAD Savunma Ve Savaş Araştırmaları Dergisi, 34(1), 137-152. https://doi.org/10.54078/savsad.1406855