SavedAdd your journal

Journals / Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi / 2026 / Cilt: 41 Sayı: 1

Evaluation of strategic targeted projects through multi-criteria decision-making methods and workload optimization: a case study in defense industry

Stratejik hedefli projelerin çok kriterli karar verme yöntemleri ile değerlendirilmesi ve iş yükü optimizasyonu: Savunma sanayi örneği

Metehan Kantemir(GAZİ ÜNİVERSİTESİ, BİLİŞİM ENSTİTÜSÜ, YÖNETİM BİLİŞİM SİSTEMLERİ ANABİLİM DALI)0009-0001-5942-7469
Tahsin Çetinyokuş(Gazi üniversitesi)0000-0002-9963-5174
Pages
307–322
Publication Date
2026-02-20
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The research findings provide concrete recommendations for rationalizing decision-making processes, improving resource allocation, and managing uncertainties in defense industry projects.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study highlights the structural differences among the projects of the Presidency of Defence Industries (SSB) in terms of size, scope, budget, and timeline, revealing that these projects inherently involve a high degree of uncertainty.
📊
Key Findings & Data:It emphasizes that such uncertainties pose significant risks from a project management perspective.
💡
Conclusion & Impact:The research findings provide concrete recommendations for rationalizing decision-making processes, improving resource allocation, and managing uncertainties in defense industry projects.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Stratejik proje değerlendirmesi · çok kriterli karar verme · proje yönetimi

Abstract

This study highlights the structural differences among the projects of the Presidency of Defence Industries (SSB) in terms of size, scope, budget, and timeline, revealing that these projects inherently involve a high degree of uncertainty. It emphasizes that such uncertainties pose significant risks from a project management perspective. The primary objective of this research is to develop a novel method that enables project planning processes to be carried out more effectively, systematically, and based on quantitative foundations, by accounting for the aforementioned uncertainties and risk factors. Within this scope, projects have been classified according to their descriptive characteristics, and a complexity coefficient has been calculated for each project. These coefficients have been utilized as a fundamental parameter in the development of decision support systems (DSS), in conjunction with multi-criteria decision-making (MCDM) methods. As part of the DSS, personnel workload analysis has also been conducted. The proposed model offers a planning and management perspective aligned with the strategic importance of defense industry projects, while also presenting an original and practice-oriented framework to support the management of uncertainties encountered in such projects. The implementation study conducted tests the validity of the model and aims to address significant gaps in the existing literature. The research findings provide concrete recommendations for rationalizing decision-making processes, improving resource allocation, and managing uncertainties in defense industry projects.

Özet

Bu çalışma, Savunma Sanayii Başkanlığı (SSB) projelerinin büyüklük, kapsam, bütçe ve zamanlama açısından gösterdiği yapısal farklılıkların, projelerin doğasında yüksek düzeyde belirsizlik barındırdığını ortaya koymakta ve bu belirsizliklerin proje yönetimi açısından önemli riskler oluşturduğunu vurgulamaktadır. Araştırmanın temel amacı, söz konusu belirsizlik ve risk unsurlarını dikkate alarak, proje planlama süreçlerinin daha etkin, sistematik ve niceliksel temellere dayalı bir yaklaşımla gerçekleştirilmesini mümkün kılacak özgün bir yöntem geliştirmektir. Bu kapsamda, projeler tanımlayıcı özelliklerine göre sınıflandırılmış ve her bir proje için bir karmaşıklık katsayısı hesaplanmıştır. Elde edilen bu katsayılar, çok kriterli karar verme (ÇKKV) yöntemleri ile birlikte kullanılarak karar destek sistemlerinin (KDS) oluşturulmasında temel bir parametre olarak değerlendirilmiştir. KDS kapsamında personel iş yükü analizi de gerçekleştirilmiştir. Geliştirilen model, savunma sanayii projelerinin stratejik önemine uygun bir planlama ve yönetim bakış açısı sunarken; aynı zamanda, bu projelerde karşılaşılan belirsizliklerin yönetimine katkı sağlayacak nitelikte özgün ve uygulamaya dönük bir çerçeve ortaya koymaktadır. Gerçekleştirilen uygulama çalışması, modelin geçerliliğini test etmekte ve mevcut literatürdeki önemli boşluklara katkı sunmayı hedeflemektedir. Araştırma bulguları, savunma sanayii projelerinde karar alma süreçlerinin rasyonelleştirilmesi, kaynakların daha etkin tahsisi ve belirsizliklerin yönetilmesine yönelik somut öneriler ortaya koymaktadır.

🔖

Cite This Article

Kantemir, M., Çetinyokuş, T. (2026). Evaluation of strategic targeted projects through multi-criteria decision-making methods and workload optimization: a case study in defense industry. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, Cilt: 41 Sayı: 1, 307–322. https://doi.org/10.17341/gazimmfd.1765317

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (57)

  1. Yozgat, U. Proje yönetiminde başarı. Öneri Dergisi, 3 (13), 5–8, 2000.
  2. Pich, M. T., Loch, C. H. ve Meyer, A. D. On uncertainty, ambiguity, and complexity in project management. Management Science, 48 (8), 1008–1023, 2002.
  3. Herroelen, W. Project scheduling—Theory and practice. Production and Operations Management, 14 (4), 413–432, 2005.
  4. Kolisch, R. ve Hartmann, S. Resource-constrained project scheduling: An experimental investigation of heuristic scheduling techniques. Management Science, 45 (11), 1485–1501, 1999.
  5. McAfee, A. ve Brynjolfsson, E. Big data: The management revolution. Harvard Business Review, 90 (10), 60–68, 2012.
  6. Koçoğlu, E. ve Ersöz, F. Data mining application for financial decision optimization at risk. International Journal of 3D Printing Technology and Digital Industry, 5 (2), 195–209, 2021.
  7. Power, D. J. ve Sharda, R. Model-driven decision support systems: Concepts and research directions. Decision Support Systems, 43 (3), 1044–1061, 2007.
  8. Turban, E., Sharda, R., Delen, D., Aronson, J. E. ve Liang, T.-P. Decision Support and Business Intelligence Systems, 9th ed., Pearson Education, Upper Saddle River, NJ, USA, 2011.
  9. Zhang, D. ve Nunamaker, J. F. Jr. Powering e-learning in the new millennium: An overview of e-learning and enabling technology. Information Systems Frontiers, 5 (2), 207–218, 2003.
  10. Mora, M., Forgionne, G. ve Gupta, J. N. D. Decision Making Support Systems: Achievements and Challenges for the New Decade, Idea Group Publishing, Hershey, PA, USA, 2003.
  11. Simon, H. A. The New Science of Management Decision, Rev. ed., Prentice Hall, Englewood Cliffs, NJ, USA, 1977.
  12. Belton, V. ve Stewart, T. J. Multiple Criteria Decision Analysis: An Integrated Approach, Kluwer Academic Publishers, Dordrecht, Netherlands, 2002.
  13. Saaty, T. L. The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation, McGraw-Hill, New York, NY, USA, 1980.
  14. Forman, E. H. ve Gass, S. I. The analytic hierarchy process—An exposition. Operations Research, 49 (4), 469–486, 2001.
  15. Saaty, T. L. Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1 (1), 83–98, 2008.
  16. Saaty, T. L. Decision Making with Dependence and Feedback: The Analytic Network Process, RWS Publications, Pittsburgh, PA, USA, 1996.
  17. Saaty, T. L. ve Takizawa, M. Dependence and feedback in decision-making: The analytic network process. European Journal of Operational Research, 26 (1), 229–244, 1986.
  18. Saaty, T. L. ve Vargas, L. G. Decision Making with the Analytic Network Process: Economic, Political, Social and Technological Applications with Benefits, Opportunities, Costs and Risks, Springer, Boston, MA, USA, 2006.
  19. Hwang, C. L. ve Yoon, K. Multiple Attribute Decision Making: Methods and Applications, Springer-Verlag, Berlin, Germany, 1981.
  20. Chen, C.-T. Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114 (1), 1–9, 2000.
  21. Opricovic, S. ve Tzeng, G. H. Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156 (2), 445–455, 2004.
  22. Karaoğlan, A. D. Multi-criteria decision making using the VIKOR method: An application in the logistics sector. Çukurova University Journal of Social Sciences, 25 (1), 101–118, 2016.
  23. Brans, J. P. ve Vincke, P. A preference ranking organization method: The PROMETHEE method for multiple criteria decision-making. Management Science, 31 (6), 647–656, 1985.
  24. Roy, B. Multicriteria Methodology for Decision Aiding, Kluwer Academic Publishers, Dordrecht, Netherlands, 1996.
  25. Brauers, W. K. M. ve Zavadskas, E. K. The MOORA method and its application to privatization in a transition economy. Control and Cybernetics, 35 (2), 445–469, 2006.
  26. Gabus, A. ve Fontela, E. World Problems: A New Approach for More Thought, Battelle Geneva Research Center, Geneva, Switzerland, 1972.
  27. Li, R. ve Tzeng, G. H. Identification of a threshold value for the DEMATEL method. Expert Systems with Applications, 36 (3), 5204–5210, 2009.
  28. Roubens, M. Preference relations on actions and criteria in multicriteria decision making. European Journal of Operational Research, 10 (1), 51–55, 1982.
  29. Keršulienė, V., Zavadskas, E. K. ve Turskis, Z. Selection of rational dispute resolution method. Journal of Business Economics and Management, 11 (2), 243–258, 2010.
  30. Zopounidis, C. ve Doumpos, M. Multicriteria classification and sorting methods. European Journal of Operational Research, 138 (2), 229–231, 2002.
  31. Nemery, P. ve Lamboray, C. FlowSort: A flow-based method for sorting problems. Computers and Operations Research, 35 (11), 3404–3420, 2008.
  32. Dalkey, N. C. ve Helmer, O. An experimental application of the Delphi method to the use of experts. Management Science, 9 (3), 458–467, 1963.
  33. Linstone, H. A. ve Turoff, M. The Delphi Method: Techniques and Applications, Addison-Wesley, Reading, MA, USA, 1975.
  34. Rowe, G. ve Wright, G. The Delphi technique as a forecasting tool. International Journal of Forecasting, 15 (4), 353–375, 1999.
  35. Srinivasan, V. ve Shocker, A. D. Linear programming techniques for multidimensional analysis of preferences. Psychometrika, 38 (3), 337–369, 1973.
  36. Diakoulaki, D., Mavrotas, G. ve Papayannakis, L. Determining objective weights in multiple criteria problems: The CRITIC method. Computers and Operations Research, 22 (7), 763–770, 1995.
  37. Stević, Ž., Pamučar, D., Puška, A. ve Chatterjee, P. Sustainable supplier selection using a new MCDM method. Computers and Industrial Engineering, 140, 106231, 2020.
  38. Erdoğan, B. ve Aydın, Y. Performance analysis of insurance companies traded on BIST: The MARCOS method. Turkish Research Journal of Academic Social Science, 6 (2), 225–232, 2024.
  39. Han, J., Kamber, M. ve Pei, J. Data Mining: Concepts and Techniques, 3rd ed., Morgan Kaufmann, Burlington, MA, USA, 2012.
  40. Kantemir, M. Stratejik hedefli projelerin çok kriterli karar verme yöntemleri ile değerlendirilmesi ve iş yükü optimizasyonu: Savunma sanayi örneği, Yüksek Lisans Tezi, Gazi Üniversitesi Bilişim Enstitüsü, Ankara, Türkiye, 2025.
  41. Jain, A. K. Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31 (8), 651–666, 2010.
  42. Bishop, C. M. Pattern Recognition and Machine Learning, Springer, New York, NY, USA, 2006.
  43. Lessmann, S., Baesens, B., Seow, H. V. ve Thomas, L. C. Benchmarking classification algorithms for credit scoring. European Journal of Operational Research, 247 (1), 124–136, 2015.
  44. Hosmer, D. W. ve Lemeshow, S. Applied Logistic Regression, 2nd ed., Wiley, New York, NY, USA, 2000. 45. Cortes, C. ve Vapnik, V. Support-vector networks. Machine Learning, 20 (3), 273–297, 1995.
  45. John, G. H. ve Langley, P. Estimating continuous distributions in Bayesian classifiers. Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, Montreal, Canada, 338–345, 1995.
  46. Breiman, L. Random forests. Machine Learning, 45 (1), 5–32, 2001.
  47. McLachlan, G. ve Peel, D. Finite Mixture Models, Wiley, New York, NY, USA, 2000.
  48. MacQueen, J. Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, 281–297, University of California Press, Berkeley, CA, USA, 1967.
  49. Winston, W. L. Operations Research: Applications and Algorithms, 4th ed., Cengage Learning, Belmont, CA, USA, 2004.
  50. Hillier, F. S. ve Lieberman, G. J. Introduction to Operations Research, 10th ed., McGraw-Hill Education, New York, NY, USA, 2014.
  51. Boyd, S. ve Vandenberghe, L. Convex Optimization, Cambridge University Press, Cambridge, UK, 2004.
  52. Dantzig, G. B. Linear Programming and Extensions, Princeton University Press, Princeton, NJ, USA, 1963.
  53. Nocedal, J. ve Wright, S. J. Numerical Optimization, 2nd ed., Springer, New York, NY, USA, 2006.
  54. Uzuner M., Dengiz O., Kara İ. ve Karasakal E. A multi-objective mixed integer linear programming model for electric vehicle charging network design, Gazi University Journal of Engineering and Architecture Faculty, 40 (3), 1875–1886, 2025.
  55. Papadimitriou, C. H. ve Steiglitz, K. Combinatorial Optimization: Algorithms and Complexity, Dover Publications, New York, NY, USA, 1998.
  56. Hocaoğlu M.F., Tosun E. Consensus among multiple criteria decision-making methods: use of methods as voters, Gazi University Journal of Engineering and Architecture Faculty, 40 (1), 103–120, 2024.
  57. Baydan E., Yenigül S.B., Gürel Z.A., New approaches to the rural-urban dilemma: Clustering of settlement units using the K-means algorithm, Gazi University Journal of Engineering and Architecture Faculty, 40 (3), 1467–1478, 2025.