A multi-criteria decision-making (MCDM) model in the security forces operations based on rough sets

  • Zoran Karavidić University of Defence in Belgrade, Military academy, Department of Management, Belgrade, Serbia
  • Damir Projović University of Defence in Belgrade, Military academy, Department of Management, Belgrade, Serbia
Keywords: Multi-criteria Decision-making, Rough Sets, Course of Action, ROSETTA, ROSE2


The paper points to a multi-criteria decision-making model based on the rough set theory application. The model demonstrates exceptional importance of the software application of the rough sets to decision-making in the security forces operations. Applying the rough sets represents a useful tool when the data, needed for the decision-making process, include vagueness and uncertainty. By applying the model based on the applicative use of the rough sets, specific decision-making rules are formulated. These rules guide the decision-makers through the complete process of planning the security operations


Download data is not yet available.


Abbas, Z., & Burney, A. (2016). A survey of software packages used for Rough Set Analysis. Journal of Computer and Communications, 4 (9), 10-18.

Boričić, B. R., & Konjikušić, S. (2004). Logika preferencija na grubim i rasplinutim skupovima. Economic Annals, 44 (160), 131-146.

Božanić, D. I., Pamučar, D. S., & Karović, S. M. (2016). Use of the fuzzy AHP-MABAC hybrid model in ranking potential locations for preparing laying-up positions. Military Technical Courier, 64 (3), 705-729.

Burney, A., & Abbas, Z. (2015). Applications of Rough Sets in Health Sciences and Disease Diagnosis. Recent Researches in Applied Computer Science, 8 (3), 153-161.

Chen, H., Li, T., Luo, C., Horng, S. J., & Wang, G. (2015). A decision-theoretic rough set approach for dynamic data mining. IEEE Transactions on Fuzzy Systems, 23 (6), 1958-1970.

Chowdhary, C. L., & Acharjya, D. P. (2016). A hybrid scheme for breast cancer detection using intuitionistic fuzzy rough set technique. International Journal of Healthcare Information Systems and Informatics (IJHISI), 11 (2), 38-61.

Čupić, M., & Suknović, M. (2010). Teorija odlučivanja, Beograd, FON, 227-236.

Deshpande, M., & Bajaj, P. (2017). Performance Improvement of Traffic Flow Prediction Model using Combination of Support Vector Machine and Rough Set. International Journal of Computer Applications, 163 (2), 31-35.

Dobrilovic, D., Brtka, V., Berkovic, I., & Odadzic, B. (2012). Evaluation of the virtual network laboratory exercises using a method based on the rough set theory. Computer Applications in Engineering Education, 20(1), 29-37.

Durairaj, M., & Sathyavathi, T. (2013). Applying rough set theory for medical informatics data analysis. ISROSET-International Journal of Scientific Research in Computer Science and Engineering, 1, 1-8.

Gigović, L., Pamučar, D., Bajić, Z., & Milićević, M. (2016). The Combination of Expert Judgment and GIS-MAIRCA Analysis for the Selection of Sites for Ammunition Depots. Sustainability, 8(4), 372, 1-25.

Gordic, M., Slavkovic, R., & Talijan, M. (2013). A conceptual model of the state security system using the modal experiment. “Carol I” National Defence University Publishing House, 48(3), 58-67.

Grabowski, A. (2016). Lattice theory for rough sets–a case study with Mizar. Fundamenta Informaticae, 147 (2-3), 223-240.

Greco, S., Matarazzo, B., & Slowinski, R. (2002). Rough sets methodology for sorting problems in presence of multiple attributes and criteria. European journal of operational research, 138 (2), 247-259.

Jaddi, N. S., & Abdullah, S. (2013). Hybrid of genetic algorithm and great deluge algorithm for rough set attribute reduction. Turkish Journal of Electrical Engineering & Computer Sciences, 21 (6), 1737-1750.

Ji, Z., Sun, Q., Xia, Y., Chen, Q., Xia, D., & Feng, D. (2012). Generalized rough fuzzy c-means algorithm for brain MR image segmentation. Computer methods and programs in biomedicine, 108 (2), 644-655.

Jia, X., Shang, L., Ji, Y., & Li, W. (2007). An incremental updating algorithm for core computing in dominance-based rough set model. In International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing. Springer Berlin Heidelberg.

Jiang, F., Zhou, K., Deng, H., Li, X., & Zhong, Y. (2009). An Optimized Model for Blasting Parameters in Underground Mines' Deep-Hole Caving Based on Rough Set and Artificial Neural Network. In Computational Intelligence and Design, 2009. ISCID'09. Second International Symposium on IEEE, 1, 459-462.

Johnson, D. S. (1974). Approximation algorithms for combinatorial problems. Journal of computer and system sciences, 9(3), 256-278.

Komorowski, J., Øhrn, A. and Skowron, A. (2002) Case Studies: Public Domain, Multiple Mining Tasks Systems: Rosetta Rough Sets. In: Zyt, J., Klosgen, W. and Zytkow, J.M., Eds., Handbook of Data Mining and Knowledge Discovery, Oxford University Press Inc., Oxford, 554-559.

Kuburić, M., Ćirović, G., & Kapović, Z. (2012). Estimation of bridges through implementation of rough sets theory. Technical Gazette, 19(4), 781-793.

Li, X. (2014). Attribute selection methods in Rough Set Theory. Master's Projects 352. San José State University.

Liang, J., Wang, F., Dang, C., & Qian, Y. (2014). A group incremental approach to feature selection applying rough set technique. IEEE Transactions on Knowledge and Data Engineering, 26 (2), 294-308.

Pamučar, D., Božanić, D., & Đorović, B. (2011). Modelling of the fuzzy logical system for offering support in making decisions within the engineering units of the Serbian army. International journal of physical sciences, 6 (3), 592-609.

Pawlak, Z. (1982). Rough sets. International Journal of Parallel Programming, 11 (5), 341-356.

Pawlak, Z. (2002). Rough sets and intelligent data analysis. Information sciences, 147 (1), 1-12.

Prędki, B. and Wilk, S. (1999) Rough Set Based Data Exploration Using ROSE System. 11th International Symposium of Foundations of Intelligent Systems, Warsaw, 8-11, 172-180.

Predki, B., Słowiński, R., Stefanowski, J., Susmaga, R., & Wilk, S. (1998). ROSE-software implementation of the rough set theory. In International Conference on Rough Sets and Current Trends in Computing. Springer, Berlin, Heidelberg.

Romański, S. (1988). Operations on families of sets for exhaustive search, given a monotonic function. In Proceedings of the Third International Conference on Data and Knowledge Bases: Improving Usability and Responsiveness, 310-322.

Shen, K. Y., Sakai, H., & Tzeng, G. H. (2017). Stable Rules Evaluation for a Rough-Set-Based Bipolar Model: A Preliminary Study for Credit Loan Evaluation. In International Joint Conference on Rough Sets. Springer, Cham.

Shen, L., & Chen, S. (2013). Research of customer classification based on rough set using rosetta software. In Proceedings of the 2012 International Conference on Communication, Electronics and Automation Engineering. Springer Berlin Heidelberg.

Skowron, A., & Rauszer C. (1992). The discernibility matrices and functions in information systems in: Slowinski R. Intelligent Decision Support. Handbook of Applications and Advances of the Rough Sets Theory. Kluwer Academic Publishers.

Slavkovic, R., Talijan, M., & Jelic, M. (2012). Operatics in the system of defence sciences (military sciences). “Carol I” National Defence University Publishing House, 45(4), 88-100.

Slavkovic, R., Talijan, M., & Jelic, M. (2013). Relationship between theory and doctrine of operational art. Security and Defence Quarterly, 1(1), 54-75.

Stokić, E., Brtka, V., & Srdić, B. (2010). The synthesis of the rough set model for the better applicability of sagittal abdominal diameter in identifying high risk patients. Computers in Biology and Medicine, 40 (9), 786-790.

Suknović, M., & Delibašić, B. (2010). Poslovna inteligencija i sistemi za podršku odlučivanju. FON, Beograd.

Tamai, S. (2009). Tools for Operational Planning Functional Area Service: What Is This. NRDC-ITA Magazine.

US Army. (2015). FM 6-0 Commander and staff organization and operations, Washington.

Wroblewski, J. (1995). Finding minimal reducts using genetic algorithms. In Proccedings of the second annual join conference on infromation science, 2, 186-189.

How to Cite
Karavidić, Z., & Projović, D. (2018). A multi-criteria decision-making (MCDM) model in the security forces operations based on rough sets. Decision Making: Applications in Management and Engineering, 1(1), 97-120. Retrieved from https://dmame.rabek.org/index.php/dmame/article/view/10