LOGISTIKA TIZIMIDA YUK OQIMLARINI PROGNOZLASH VA MARSHRUTLARNI DINAMIK OPTIMALLASHTIRISHNING SUN'IY INTELLEKTGA ASOSLANGAN GIBRID (LSTM–ACO) MODELI
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Abstract
Ushbu tezisda logistika tizimida yuk oqimlarini prognozlash va tashish marshrutlarini real vaqt rejimida dinamik optimallashtirish muammosini hal etish uchun sun’iy intellektga asoslangan gibrid model taklif etiladi. Model ikki modulni birlashtiradi: yuk hajmini ko‘p omilli vaqt qatorlari asosida bashorat qiluvchi LSTM neyron tarmog‘i va prognoz hamda real vaqtdagi tirbandlik ma’lumotlari asosida marshrutlarni qayta taqsimlovchi modifikatsiyalangan muravyalar koloniyasi algoritmi (Ant Colony Optimization, ACO). O‘zbekistonning transport-logistika tizimini 2030-yilgacha rivojlantirish konsepsiyasida belgilangan vazifalar — xalqaro yuk tashish hajmini kamida 2 baravar oshirish va Jahon banki Logistika samaradorligi indeksi (LPI) bo‘yicha mamlakat o‘rnini yaxshilash — nuqtai nazaridan yondashuvning amaliy dolzarbligi asoslanadi. Modelning arxitekturasi, matematik rasmiylashtirilishi va baholash mezonlari tavsiflanadi.
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