TRANSPORT-LOGISTIKA TIZIMIDA YUK OQIMLARINI PROGNOZLASH VA MARSHRUTLARNI ADAPTIV BOSHQARISHNING SUN'IY INTELLEKTGA ASOSLANGAN GIBRID (LSTM–RL) MODELI
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Ushbu tezisda yuk oqimlarini prognozlash va marshrutlarni adaptiv boshqarish muammosini hal etish uchun sun'iy intellektga asoslangan gibrid model taklif etiladi. Model ikki modulni birlashtiradi: talabni ko'p omilli vaqt qatorlari asosida bashorat qiluvchi LSTM tarmog'i va marshrutlarni real vaqtda qayta taqsimlovchi mustahkamlangan o'rganish (RL) agenti. Muammo Markov qaror qabul qilish jarayoni (MDP) sifatida rasmiylashtirilib, holat, harakat va mukofot funksiyalari aniq belgilanadi. O'zbekistonda transport-logistika tizimini 2030yilgacha rivojlantirish konsepsiyasida belgilangan vazifalar — yuk tashish hajmini 2 baravar oshirish va LPI ko'rsatkichini yaxshilash — nuqtai nazaridan yondashuvning amaliy dolzarbligi asoslanadi. Modelning arxitekturasi, matematik rasmiylashtirilishi va baholash mezonlari tavsiflanadi.
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