NOANIQLIK SHAROITIDA KUCHLARNI SAMARALI BOSHQARISH BO‘YICHA QAROR QABUL QILISHGA KO‘MAKLASHUVCHI ALGORITM
Keywords:
ziddiyatli vaziyat, kuchlar soni, jangovar imkoniyat, taktik yechimlar, noaniq mantiq, a’zolik funksiyasi, qaror qabul qilish, algoritmAbstract
Hozirgi zamonaviy harbiy harakatlarda komandirlarning tezkorlik, aniqlik va noaniqlik ostida qaror qabul qilish qobiliyati katta ahamiyat kasb etmoqda. Zamonaviy jangovar harakatlarda qaror qabul qilish jarayonida noaniqlik, noma’lum parametrlar va dinamik o‘zgaruvchan sharoitlar mavjudligi strategik va taktik yechimlarni
qabul qilishni murakkablashtiradi. Bunday sharoitda harbiy harakatlarni samarali rejalashtirish va boshqarish ko‘pincha real vaqt rejimida va cheklangan resurslar doirasida optimal qarorlar qabul qilishni talab etadi. Bu esa axborot oqimlarini tahlil qilish, modellashtirish va qarorlarni qo‘llab-quvvatlovchi algoritm va dasturiy vositalarni ishlab chiqishni taqozo etadi. Ushbu maqolada Lanchester kvadratik modelining natijalaridan kelib chiqqan taktik yechimlarni noaniqlik sharoitida qaror qabul qilish jarayonlariga moslashtirgan holda qaror qabul qilish muammosi o‘rganilgan. Xususan, jang modeli asosida shakllantirilgan taktik yechimlar noaniq mantiq (fuzzy logic) metodologiyasi asosida tahlil qilinib, jangovar parametrlar (kuchlar soni, jangovar imkoniyat) to‘liq aniqlanmagan yoki ehtimolli bo‘lgan holatlarda ham qaror qabul qiluvchi tomonga eng maqbul taktikani tanlashga ko‘maklashuvchi algoritm ishlab chiqilgan. O‘tkazilgan simulyatsiya natijalari taklif etilgan algoritmning samaradorligi va amaliy ahamiyatini ishonchli tarzda tasdiqladi.
References
1. Sha J. C. Mathematic Tactics. Beijing: Science Press, 2003. – P. 14-36.
2. Mamatov M., Karimov N., Muminov S., Sharipova Sh. Numerical solution algorithm for the model of combat operations under the influence of time delay // Journal of Interdisciplinary Mathematics. Volume 28, Issue, 5. August 2025. P. 2011 – 2023. DOI:10.47974/JIM-2239
3. DeBerry WT, Dill R, Hopkinson K, Hodson DD, Grimaila M. The wargame commodity course of action automated analysis method // The Journal of Defense Modeling and Simulation. 21(1). 2021. P. 17-29.
4. Pescatore M, Beery P. Interoperability analysis via agent-based simulation // The Journal of Defense Modeling and Simulation. 21(1). 2022. – P. 103-116.
5. Bonder S. Army operations research - historical perspectives and lessons learned // Operation Research. Vol. 50, No. 1. 2002. – P. 25–34.
6. John Yen, Reza Langari. Fuzzy Logic: Intelligence, Control, and Information // Prentice Hall, Chapter 1-7. 1999. – P. 1-167.
7. Timothy Ross. Fuzzy Logic with Engineering Applications // John Wiley & Sons Inc, Chapter 1-4. 2004. – P. 1-114.
8. George J. Klir and Bo Yuan. Fuzzy Sets and Fuzzy Logic: Theory and Applications // Prentice Hall, Chapter 1-9. 1995. – P. 1-278.
9. M.Ganesh. Introduction To Fuzzy Sets And Fuzzy Logic // Prentice-hall, Chapter 1-8. 2008. – P. 1-166.
10. Karimov N.M. The Importance of Lanchester's Quadratic Law in Developing Combat Tactics for Conflict Situations. University of Public Security of the Republic of Uzbekistan. Scientific and Practical Journal of Public Security. Volume 2, 2022. – P. 110-118.
11. Hakan Ayhan Dağistanli. Weapon system selection for capability-based defense planning using Lanchester models integrated with fuzzy MCDM in computer assisted military experiment // Knowledge and Decision Systems with Applications Vol. 1. 2025. P. 11-23.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Akbaraliyev Baxtiyorjon Bakirovich, Karimov Nodirbek Madirimovich

This work is licensed under a Creative Commons Attribution 4.0 International License.







