2023/11/13 by Zangir Iklassov, Ikboljon Sobirov, Iklassov, Zangir +5 · 3 citations
Engineering · Social Sciences · #Artificial Intelligence (cs.AI) #Computational Engineering #FOS: Computer and information sciences #Finance #Machine Learning (cs.LG) #Smart Parking Systems Research #Transportation Planning and Optimization #Transportation and Mobility Innovations #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2311.07708
openalex publication_date 2023/11/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This study addresses a gap in the utilization of Reinforcement Learning (RL) and Machine Learning (ML) techniques in solving the Stochastic Vehicle Routing Problem (SVRP) that involves the challenging task of optimizing vehicle routes under uncertain conditions. We propose a novel end-to-end framework that comprehensively addresses the key sources of stochasticity in SVRP and utilizes an RL agent with a simple yet effective architecture and a tailored training method. Through comparative analysis, our proposed model demonstrates superior performance compared to a widely adopted state-of-the-art metaheuristic, achieving a significant 3.43% reduction in travel costs. Furthermore, the model exhibits robustness across diverse SVRP settings, highlighting its adaptability and ability to learn optimal routing strategies in varying environments. The publicly available implementation of our framework serves as a valuable resource for future research endeavors aimed at advancing RL-based solutions for SVRP.