A reinforcement learning model for route allocation optimization: A case study of C-130H transport aircraft in the Royal Thai Air Force
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Abstract
This study aims to develop a route-allocation model for transport aircraft in the Royal Thai Air Force's (RTAF) air logistics operations. A Reinforcement Learning (RL) approach was applied to optimize resource allocation by determining the most suitable routes based on cargo capacity distribution, thereby reducing operational flight distances and, consequently, the frequency of aircraft maintenance. This research was conducted in a simulated environment using domestic air transport data as a reference for C-130H transport aircraft of Squadron 601, Wing 6, RTAF. Experimental results show that the developed model significantly improves air transport operational efficiency in support and service provision, facilitating network operations by reducing flight distances and increasing process continuity under varying cargo capacity conditions compared to current practices. Ultimately, this contributes to the improvement and sustainability of defense operations. The proposed work schedule also demonstrates adaptability to dynamic operational constraints and changing demand.
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