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SURVEY ON MILITARY OPERATIONS OF FUZZY SET THEORY AND ITS APPLICATIONS

Year 2020, Volume 16, Issue 2, 117 - 141, 11.11.2020

Abstract

In recent years, with the increase in investment in countries' military expenditures, the importance of decision-making problems in the military has gradually increased. There are various uncertainties such as vagueness, imprecise or ambiguous in decision-making problems. Decision making is the process of evaluating environmental factors from a holistic perspective, identifying problems and possible alternatives, making a systematic assessment, and identifying the most appropriate decision among them. The aim of this study is to investigate various types of fuzzy set that can be used in military problems under uncertainty. In addition, a detailed literature review of fuzzy decision making problems applied in the military are conducted.

References

  • Afful-Dadzie, E., Oplatkova, Z. K., & Prieto, L. A. B. (2017). “Comparative state-of-the-art survey of classical fuzzy set and intuitionistic fuzzy sets in multi-criteria decision making”, International Journal of Fuzzy Systems, 19(3), 726-738.
  • Arulkumaran, G., & Gnanamurthy, R. K. (2019). “Fuzzy trust approach for detecting black hole attack in mobile adhoc network”, Mobile Networks and Applications, 24(2), 386-393.
  • Ashtiani, B., Haghighirad, F., Makui, A., & ali Montazer, G. (2009). “Extension of fuzzy TOPSIS method based on interval-valued fuzzy sets”, Applied Soft Computing, 9(2), 457-461.
  • Bean, W. L., Joubert, J. W., & Luhandjula, M. K. (2016). “Inventory management under uncertainty: A military application”, Computers & Industrial Engineering, 96, 96-107.
  • Braathen, S., & Sendstad, O. J. (2004). “A hybrid fuzzy logic/constraint satisfaction problem approach to automatic decision making in simulation game models”, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34(4), 1786-1797.
  • Bustince Sola, H., Fernandez, J., Hagras, H., Herrera, F., Pagola, M., & Barrenechea, E. (2014). “Interval type-2 fuzzy sets are generalization of interval-valued fuzzy sets: Toward a wider view on their relationship”, IEEE Transactions on Fuzzy Systems, 23(5), 1876-1882.
  • Cheng, C. H. (1999). “Evaluating weapon systems using ranking fuzzy numbers”, Fuzzy sets and systems, 107(1), 25-35.
  • David, V. R., Octavio, G. R., David, B. M., & Victor, E. C. (2015, July). Analysis, Design, and Implementation of an Autopilot for Unmanned Aircraft-UAV's Based on Fuzzy Logic. In Asia-Pacific Conference on Computer Aided System Engineering (pp. 196-201). IEEE.
  • Deveci, M., Akyurt, I. Z., & Yavuz, S. (2018). “A GIS-based interval type-2 fuzzy set for public bread factory site selection”, Journal of Enterprise Information Management.
  • Eyoh, I., John, R., De Maere, G., & Kayacan, E. (2018). “Hybrid learning for interval type-2 intuitionistic fuzzy logic systems as applied to identification and prediction problems”, IEEE Transactions on Fuzzy Systems, 26(5), 2672-2685.
  • Figueroa García, J. C. (2015). “On the fuzzy extension principle for LP problems with Interval Type-2 Technological Coefficients”, Ingeniería, 20(1), 101-110.
  • Grattan-Guinness, “Fuzzy membership mapped onto interval and many-valuedquantities”, Z. Math. Logik Grundlag. Mathe. 22 (1975) 149–160.
  • Haesebrouck, T. (2017). “NATO burden sharing in Libya: A fuzzy set qualitative comparative analysis”, Journal of conflict resolution, 61(10), 2235-2261.
  • Haibin, W. A. N. G., Smarandache, F., Zhang, Y., & Sunderraman, R. (2010). “Single valued neutrosophic sets”, Infinite study.
  • Hagras, H. A. (2004). “A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots”, IEEE Transactions on Fuzzy systems, 12(4), 524-539.
  • Juan, W., Huapu, L., Xu, S., Xianfeng, L., & Huijun, Y. (2014). “The best path analysis in military highway transport based on DEA and multiobjective fuzzy decision-making”, Mathematical Problems in Engineering, 2014.
  • Karaköse, M., & Akın, E. (2004). Tip-1 bulanık sistemlerde tip-2 bulanık girişler, ELECO (Elektrik, Elektronik ve Bilgisayar Mühendisliği Sempozyumu ve Fuarı) Bildiriler Kitabı, 8-10.
  • Karatas, M. (2020). Hydrogen energy storage method selection using fuzzy axiomatic design and analytic hierarchy process. International Journal of Hydrogen Energy, 45(32), 16227-16238.
  • Karatas, M. (2017). “Multiattribute decision making using multiperiod probabilistic weighted fuzzy axiomatic design”, Systems Engineering, 20(4), 318-334.
  • Karatas, M., & Akman, G. (2014). An Extension to Multi-Attribute Decision Making Method: Dynamic Fuzzy Axiomatic Design Approach, In Joint International Symposium on CIE44 and IMSS’14 Proceedings.
  • Karatas, M., Razi, N., & Gunal, M. M. (2017). “An ILP and simulation model to optimize search and rescue helicopter operations”, Journal of the Operational Research Society, 68(11), 1335-1351.
  • Karatas, M., Yakıcı, E., & Razi, N. (2019). “Military facility location problems: a brief survey”, In Operations Research for Military Organizations (pp. 1-27). IGI Global.
  • Khanmohammadi, S., Dagli, C. H., & Esfahlani, F. Z. (2012). “A fuzzy inference model for predicting irregular human behaviour during stressful missions”, Procedia Computer Science, 12, 265-270.
  • Kutlu Gündoğdu, F., & Kahraman, C. (2019). “Spherical fuzzy sets and spherical fuzzy TOPSIS method”, Journal of Intelligent & Fuzzy Systems, (Preprint), 1-16.
  • Lee, L. W., & Chen, S. M. (2008, July). Fuzzy multiple attributes group decision-making based on the extension of TOPSIS method and interval type-2 fuzzy sets, In International Conference on Machine Learning and Cybernetics (Vol. 6, pp. 3260-3265). IEEE.
  • Li, D. F., Huang, Z. G., & Chen, G. H. (2010). “A systematic approach to heterogeneous multiattribute group decision making”, Computers & Industrial Engineering, 59(4), 561-572.
  • 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.
  • Lu, W. M., & Wang, T. C. (2011). “A fuzzy multi-criteria model for the industrial cooperation program transaction strategies: A case in Taiwan”, Expert Systems with Applications, 38(3), 1490-1500.
  • Mendel, J. M., & John, R. B. (2002). “Type-2 fuzzy sets made simple”, IEEE Transactions on fuzzy systems, 10(2), 117-127.
  • Mendel, J. M., John, R. I., & Liu, F. (2006). “Interval type-2 fuzzy logic systems made simple”, IEEE transactions on fuzzy systems, 14(6), 808-821.
  • Moon, C., Lee, J., & Lim, S. (2010). “A performance appraisal and promotion ranking system based on fuzzy logic: An implementation case in military organizations”, Applied Soft Computing, 10(2), 512-519.
  • Naim, S., & Hagras, H. (2012, June). A hybrid approach for multi-criteria group decision making based on interval type-2 fuzzy logic and intuitionistic fuzzy evaluation, In IEEE International Conference on Fuzzy Systems (pp. 1-8). IEEE.
  • Palaniappan, S., Zein-Sabatto, S., & Sekmen, A. (2001). “Dynamic multiobjective optimization of war resource allocation using adaptive genetic algorithms”, IEEE, pp. 160-165.
  • Salim, O. M., Abdel-Aty-Zohdy, H. S., & Zohdy, M. A. (2010, July). Hyper-fuzzy modeling and control for bio-inspired radar processing, In Proceedings of the IEEE 2010 National Aerospace & Electronics Conference (pp. 392-395). IEEE.
  • Sambuc, R. (1975). Fonctions and floues: application a`l’aide au diagnostic en pathologie thyroidienne (Doctoral dissertation). University of Marseille.
  • Sánchez-Lozano, J. M., Serna, J., & Dolón-Payán, A. (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, 58-65.
  • Sanjian, G. S. (2003). “Arms transfers, military balances, and interstate relations: Modeling power balance versus power transition linkages”, Journal of Conflict Resolution, 47(6), 711-727.
  • Schenker, D. F., & Khoshgoftaar, T. M. (1998, November). “The application of fuzzy enhanced case-based reasoning for identifying fault-prone modules”, In Proceedings Third IEEE International High-Assurance Systems Engineering Symposium (Cat. No. 98EX231) (pp. 90-97). IEEE.
  • SIPRI Military Expenditure Database, (2018). Retrieved from https://www.sipri.org/databases/milex.
  • SIPRI Yearbook (2018). Armaments, disarmament and international security. Retrieved from https://www.sipri.org/sites/default/files/2018-06/yb_18_summary_en_0.pdf
  • Smarandache, F. (1999). “A unifying field in logics. neutrosophy: neutrosophic probability, set and logic”. American Research Press.
  • Son, H. S., Park, J. B., & Joo, Y. H. (2014). “Fuzzy c-means-based intelligent tracking algorithm for an underwater manoeuvring target”, IET Radar, Sonar & Navigation, 8(9), 1042-1050.
  • Sutoyo, E., Mungad, M., Hamid, S., & Herawan, T. (2016). “An efficient soft set-based approach for conflict analysis”, PloS one, 11(2): e0148837. Retrieved from http://www.doi:10.1371/journal.pone.014883
  • Stanujkic, D., Zavadskas, E. K., Brauers, W. K., & Karabasevic, D. (2015). “An extension of the MULTIMOORA method for solving complex decision-making problems based on the use of interval-valued triangular fuzzy numbers”, Transformations in Business & Economics, 14(2B), 355-377.
  • Torra, V. (2010). “Hesitant fuzzy sets”, International Journal of Intelligent Systems, 25(6), 529-539.
  • Tozan, H., & Karatas, M. (Eds.). (2018). “Operations research for military organizations”, IGI Global, Hershey PA, USA 17033. Retrieved from http://www.doi:10.4018/978-1-5225-5513-1.
  • Tozan, H., Karatas, M., & Vayvay, O. (2018). “Reducing demand signal variability via a quantitative fuzzy grey regression approach”, Tehnički vjesnik, 25(Supplement 2), 411-419.
  • Wang, L., Wang, Q., Xu, S., & Ni, M. (2014, May). Distance and similarity measures of dual hesitant fuzzy sets with their applications to multiple attribute decision making. In IEEE International Conference on Progress in Informatics and Computing (pp. 88-92). IEEE.
  • Wilkins, D. E., & Desimone, R. V. (1993). “Applying an AI planner to military operations planning”, Sri International Menlo Park Ca (Technical No. SRI-TN-534).
  • Xia, M., & Xu, Z. (2011). “Hesitant fuzzy information aggregation in decision making”, International journal of approximate reasoning, 52(3), 395-407.
  • Yager, R. R., & Abbasov, A. M. (2013). “Pythagorean membership grades, complex numbers, and decision making”, International Journal of Intelligent Systems, 28(5), 436-452.
  • Yager, R. R. (2016). Properties and applications of Pythagorean fuzzy sets. In P. Angelov, & S. Sotirov (Eds.), In Imprecision and Uncertainty in Information Representation and Processing (pp. 119-136). Springer, Cham.
  • Yakıcı, E., Karatas, M., and Yılmaz, O. (2019). “The problem of locating and routing unmanned aerial vehicles”, In H. Tozan, & M. Karatas (Eds.), Operations Research for Military Organizations (pp. 28-53). Hershey, PA: IGI Global.
  • 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 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) (pp. 204-210). IEEE.
  • Yılmaz, O., Yakıcı, E., & Karataş, M. (2019). “A UAV location and routing problem with spatio-temporal synchronization constraints solved by ant colony optimization”, Journal of Heuristics, 25, 673-701.
  • Zadeh, L. A. (1965). “Fuzzy sets”, Information and control, 8(3), 338-353.
  • Zhang, W., Ju, Y., & Liu, X. (2017). “Multiple criteria decision analysis based on Shapley fuzzy measures and interval-valued hesitant fuzzy linguistic numbers”, Computers & Industrial Engineering, 105, 28-38.

BULANIK KÜMELER TEORİSİ VE UYGULAMALARININ ASKERİ OPERASYONLAR ÜZERİNE ARAŞTIRMALARI

Year 2020, Volume 16, Issue 2, 117 - 141, 11.11.2020

Abstract

Son yıllarda ülkelerin askeri harcamalarına yapılan yatırımların artmasıyla birlikte ordudaki karar alma sorunlarının önemi giderek artmıştır. Karar verme problemlerinde muğlaklık, kesin olmayan veya iki anlamlı gibi çeşitli belirsizlikler vardır. Karar verme, çevresel faktörleri bütüncül bir bakış açısıyla değerlendirme, sorunları ve olası alternatifleri belirleme, sistematik bir değerlendirme yapma ve aralarında en uygun kararı belirleme sürecidir. Bu çalışmanın amacı, belirsizlik altındaki askeri problemlerde kullanılabilecek çeşitli bulanık set türlerini incelemektir. Ayrıca, orduda uygulanan bulanık karar verme problemlerinin ayrıntılı bir literatür taraması yapılmaktadır.

References

  • Afful-Dadzie, E., Oplatkova, Z. K., & Prieto, L. A. B. (2017). “Comparative state-of-the-art survey of classical fuzzy set and intuitionistic fuzzy sets in multi-criteria decision making”, International Journal of Fuzzy Systems, 19(3), 726-738.
  • Arulkumaran, G., & Gnanamurthy, R. K. (2019). “Fuzzy trust approach for detecting black hole attack in mobile adhoc network”, Mobile Networks and Applications, 24(2), 386-393.
  • Ashtiani, B., Haghighirad, F., Makui, A., & ali Montazer, G. (2009). “Extension of fuzzy TOPSIS method based on interval-valued fuzzy sets”, Applied Soft Computing, 9(2), 457-461.
  • Bean, W. L., Joubert, J. W., & Luhandjula, M. K. (2016). “Inventory management under uncertainty: A military application”, Computers & Industrial Engineering, 96, 96-107.
  • Braathen, S., & Sendstad, O. J. (2004). “A hybrid fuzzy logic/constraint satisfaction problem approach to automatic decision making in simulation game models”, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34(4), 1786-1797.
  • Bustince Sola, H., Fernandez, J., Hagras, H., Herrera, F., Pagola, M., & Barrenechea, E. (2014). “Interval type-2 fuzzy sets are generalization of interval-valued fuzzy sets: Toward a wider view on their relationship”, IEEE Transactions on Fuzzy Systems, 23(5), 1876-1882.
  • Cheng, C. H. (1999). “Evaluating weapon systems using ranking fuzzy numbers”, Fuzzy sets and systems, 107(1), 25-35.
  • David, V. R., Octavio, G. R., David, B. M., & Victor, E. C. (2015, July). Analysis, Design, and Implementation of an Autopilot for Unmanned Aircraft-UAV's Based on Fuzzy Logic. In Asia-Pacific Conference on Computer Aided System Engineering (pp. 196-201). IEEE.
  • Deveci, M., Akyurt, I. Z., & Yavuz, S. (2018). “A GIS-based interval type-2 fuzzy set for public bread factory site selection”, Journal of Enterprise Information Management.
  • Eyoh, I., John, R., De Maere, G., & Kayacan, E. (2018). “Hybrid learning for interval type-2 intuitionistic fuzzy logic systems as applied to identification and prediction problems”, IEEE Transactions on Fuzzy Systems, 26(5), 2672-2685.
  • Figueroa García, J. C. (2015). “On the fuzzy extension principle for LP problems with Interval Type-2 Technological Coefficients”, Ingeniería, 20(1), 101-110.
  • Grattan-Guinness, “Fuzzy membership mapped onto interval and many-valuedquantities”, Z. Math. Logik Grundlag. Mathe. 22 (1975) 149–160.
  • Haesebrouck, T. (2017). “NATO burden sharing in Libya: A fuzzy set qualitative comparative analysis”, Journal of conflict resolution, 61(10), 2235-2261.
  • Haibin, W. A. N. G., Smarandache, F., Zhang, Y., & Sunderraman, R. (2010). “Single valued neutrosophic sets”, Infinite study.
  • Hagras, H. A. (2004). “A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots”, IEEE Transactions on Fuzzy systems, 12(4), 524-539.
  • Juan, W., Huapu, L., Xu, S., Xianfeng, L., & Huijun, Y. (2014). “The best path analysis in military highway transport based on DEA and multiobjective fuzzy decision-making”, Mathematical Problems in Engineering, 2014.
  • Karaköse, M., & Akın, E. (2004). Tip-1 bulanık sistemlerde tip-2 bulanık girişler, ELECO (Elektrik, Elektronik ve Bilgisayar Mühendisliği Sempozyumu ve Fuarı) Bildiriler Kitabı, 8-10.
  • Karatas, M. (2020). Hydrogen energy storage method selection using fuzzy axiomatic design and analytic hierarchy process. International Journal of Hydrogen Energy, 45(32), 16227-16238.
  • Karatas, M. (2017). “Multiattribute decision making using multiperiod probabilistic weighted fuzzy axiomatic design”, Systems Engineering, 20(4), 318-334.
  • Karatas, M., & Akman, G. (2014). An Extension to Multi-Attribute Decision Making Method: Dynamic Fuzzy Axiomatic Design Approach, In Joint International Symposium on CIE44 and IMSS’14 Proceedings.
  • Karatas, M., Razi, N., & Gunal, M. M. (2017). “An ILP and simulation model to optimize search and rescue helicopter operations”, Journal of the Operational Research Society, 68(11), 1335-1351.
  • Karatas, M., Yakıcı, E., & Razi, N. (2019). “Military facility location problems: a brief survey”, In Operations Research for Military Organizations (pp. 1-27). IGI Global.
  • Khanmohammadi, S., Dagli, C. H., & Esfahlani, F. Z. (2012). “A fuzzy inference model for predicting irregular human behaviour during stressful missions”, Procedia Computer Science, 12, 265-270.
  • Kutlu Gündoğdu, F., & Kahraman, C. (2019). “Spherical fuzzy sets and spherical fuzzy TOPSIS method”, Journal of Intelligent & Fuzzy Systems, (Preprint), 1-16.
  • Lee, L. W., & Chen, S. M. (2008, July). Fuzzy multiple attributes group decision-making based on the extension of TOPSIS method and interval type-2 fuzzy sets, In International Conference on Machine Learning and Cybernetics (Vol. 6, pp. 3260-3265). IEEE.
  • Li, D. F., Huang, Z. G., & Chen, G. H. (2010). “A systematic approach to heterogeneous multiattribute group decision making”, Computers & Industrial Engineering, 59(4), 561-572.
  • 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.
  • Lu, W. M., & Wang, T. C. (2011). “A fuzzy multi-criteria model for the industrial cooperation program transaction strategies: A case in Taiwan”, Expert Systems with Applications, 38(3), 1490-1500.
  • Mendel, J. M., & John, R. B. (2002). “Type-2 fuzzy sets made simple”, IEEE Transactions on fuzzy systems, 10(2), 117-127.
  • Mendel, J. M., John, R. I., & Liu, F. (2006). “Interval type-2 fuzzy logic systems made simple”, IEEE transactions on fuzzy systems, 14(6), 808-821.
  • Moon, C., Lee, J., & Lim, S. (2010). “A performance appraisal and promotion ranking system based on fuzzy logic: An implementation case in military organizations”, Applied Soft Computing, 10(2), 512-519.
  • Naim, S., & Hagras, H. (2012, June). A hybrid approach for multi-criteria group decision making based on interval type-2 fuzzy logic and intuitionistic fuzzy evaluation, In IEEE International Conference on Fuzzy Systems (pp. 1-8). IEEE.
  • Palaniappan, S., Zein-Sabatto, S., & Sekmen, A. (2001). “Dynamic multiobjective optimization of war resource allocation using adaptive genetic algorithms”, IEEE, pp. 160-165.
  • Salim, O. M., Abdel-Aty-Zohdy, H. S., & Zohdy, M. A. (2010, July). Hyper-fuzzy modeling and control for bio-inspired radar processing, In Proceedings of the IEEE 2010 National Aerospace & Electronics Conference (pp. 392-395). IEEE.
  • Sambuc, R. (1975). Fonctions and floues: application a`l’aide au diagnostic en pathologie thyroidienne (Doctoral dissertation). University of Marseille.
  • Sánchez-Lozano, J. M., Serna, J., & Dolón-Payán, A. (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, 58-65.
  • Sanjian, G. S. (2003). “Arms transfers, military balances, and interstate relations: Modeling power balance versus power transition linkages”, Journal of Conflict Resolution, 47(6), 711-727.
  • Schenker, D. F., & Khoshgoftaar, T. M. (1998, November). “The application of fuzzy enhanced case-based reasoning for identifying fault-prone modules”, In Proceedings Third IEEE International High-Assurance Systems Engineering Symposium (Cat. No. 98EX231) (pp. 90-97). IEEE.
  • SIPRI Military Expenditure Database, (2018). Retrieved from https://www.sipri.org/databases/milex.
  • SIPRI Yearbook (2018). Armaments, disarmament and international security. Retrieved from https://www.sipri.org/sites/default/files/2018-06/yb_18_summary_en_0.pdf
  • Smarandache, F. (1999). “A unifying field in logics. neutrosophy: neutrosophic probability, set and logic”. American Research Press.
  • Son, H. S., Park, J. B., & Joo, Y. H. (2014). “Fuzzy c-means-based intelligent tracking algorithm for an underwater manoeuvring target”, IET Radar, Sonar & Navigation, 8(9), 1042-1050.
  • Sutoyo, E., Mungad, M., Hamid, S., & Herawan, T. (2016). “An efficient soft set-based approach for conflict analysis”, PloS one, 11(2): e0148837. Retrieved from http://www.doi:10.1371/journal.pone.014883
  • Stanujkic, D., Zavadskas, E. K., Brauers, W. K., & Karabasevic, D. (2015). “An extension of the MULTIMOORA method for solving complex decision-making problems based on the use of interval-valued triangular fuzzy numbers”, Transformations in Business & Economics, 14(2B), 355-377.
  • Torra, V. (2010). “Hesitant fuzzy sets”, International Journal of Intelligent Systems, 25(6), 529-539.
  • Tozan, H., & Karatas, M. (Eds.). (2018). “Operations research for military organizations”, IGI Global, Hershey PA, USA 17033. Retrieved from http://www.doi:10.4018/978-1-5225-5513-1.
  • Tozan, H., Karatas, M., & Vayvay, O. (2018). “Reducing demand signal variability via a quantitative fuzzy grey regression approach”, Tehnički vjesnik, 25(Supplement 2), 411-419.
  • Wang, L., Wang, Q., Xu, S., & Ni, M. (2014, May). Distance and similarity measures of dual hesitant fuzzy sets with their applications to multiple attribute decision making. In IEEE International Conference on Progress in Informatics and Computing (pp. 88-92). IEEE.
  • Wilkins, D. E., & Desimone, R. V. (1993). “Applying an AI planner to military operations planning”, Sri International Menlo Park Ca (Technical No. SRI-TN-534).
  • Xia, M., & Xu, Z. (2011). “Hesitant fuzzy information aggregation in decision making”, International journal of approximate reasoning, 52(3), 395-407.
  • Yager, R. R., & Abbasov, A. M. (2013). “Pythagorean membership grades, complex numbers, and decision making”, International Journal of Intelligent Systems, 28(5), 436-452.
  • Yager, R. R. (2016). Properties and applications of Pythagorean fuzzy sets. In P. Angelov, & S. Sotirov (Eds.), In Imprecision and Uncertainty in Information Representation and Processing (pp. 119-136). Springer, Cham.
  • Yakıcı, E., Karatas, M., and Yılmaz, O. (2019). “The problem of locating and routing unmanned aerial vehicles”, In H. Tozan, & M. Karatas (Eds.), Operations Research for Military Organizations (pp. 28-53). Hershey, PA: IGI Global.
  • 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 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) (pp. 204-210). IEEE.
  • Yılmaz, O., Yakıcı, E., & Karataş, M. (2019). “A UAV location and routing problem with spatio-temporal synchronization constraints solved by ant colony optimization”, Journal of Heuristics, 25, 673-701.
  • Zadeh, L. A. (1965). “Fuzzy sets”, Information and control, 8(3), 338-353.
  • Zhang, W., Ju, Y., & Liu, X. (2017). “Multiple criteria decision analysis based on Shapley fuzzy measures and interval-valued hesitant fuzzy linguistic numbers”, Computers & Industrial Engineering, 105, 28-38.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Muhammet DEVECİ> (Primary Author)
MİLLİ SAVUNMA ÜNİVERSİTESİ, DENİZ HARP OKULU
0000-0002-3712-976X
Türkiye


Yusuf KUVVETLİ>
ÇUKUROVA ÜNİVERSİTESİ
0000-0002-9817-1371
Türkiye


İbrahim Zeki AKYURT>
Türk Havayolları Uçuş Akademisi
0000-0003-4817-5267
Türkiye

Publication Date November 11, 2020
Published in Issue Year 2020, Volume 16, Issue 2

Cite

APA Deveci, M. , Kuvvetli, Y. & Akyurt, İ. Z. (2020). SURVEY ON MILITARY OPERATIONS OF FUZZY SET THEORY AND ITS APPLICATIONS . Journal of Naval Sciences and Engineering , 16 (2) , 117-141 . Retrieved from https://dergipark.org.tr/en/pub/jnse/issue/57757/781815