Browsing by Author "Mustakhov T."

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    Reinforcement Learning Methods for solving combinatorial optimization problems
    (2023) Mustakhov T.
    The use of reinforcement learning approaches to resolve combinatorial optimization issues in the context of vehicle routing is examined in this paper. Three previously published articles’ insights are combined in one study. The Stochastic Dynamic Vehicle Routing Problem is addressed in the first paper with a deep reinforcement learning strategy. To learn the routing strategy for a single truck as customer orders come in over time, a fully attention-based model with a dynamic encoder and decoder is introduced. The model is taught using reinforcement learning, in which a reward signal influences the choice of nodes to visit. Computational analyses show that this tactic outperforms comparison algorithms. A thorough overview of machine learning techniques used to address NP-hard Vehicle Routing Problems is presented in the second paper. A variety of learning paradigms, solution structures, underlying models, and algorithms are included in the survey. It demonstrates the benefits of machine learning-based models that use the symmetry of VRP solutions and their parity with conventional approaches. The directions for future studies to address the problems with contemporary transportation systems are also described. The third paper provides a thorough analysis of the stochastic dynamic vehicle routing problem, examining state-of-the-art approaches and methodologies for solving this challenging optimization problem. The stochastic and dynamic restrictions that present significant challenges to efficient route planning and optimization are covered in detail. The study offers a thorough grasp of cutting-edge techniques and recommends prospective directions for further investigation. In addition, the use of Markov decision processes and sophisticated reinforcement learning techniques to address the issue of stochastic dynamic vehicle routing is examined. Combining these articles, this thesis advances knowledge of reinforcement learning methods for combinatorial optimization issues, particularly in the area of vehicle routing. The study demonstrates the effectiveness of deep reinforcement learning and machine learning techniques while also highlighting areas that need additional study and development in the field of transportation logistics.
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    STOCHASTIC DYNAMIC VEHICLE ROUTING PROBLEM SURVEY
    (СДУ хабаршысы - 2023, 2023) Akhmetbek Y.; Mustakhov T.
    Abstract. The present article aims to offer an exhaustive and in-depth investigation of the Stochastic Dynamic Vehicle Routing Problem, which remains a significant challenge in the field of transportation logistics. To achieve this objective, we will undertake a meticulous analysis of the latest cutting-edge techniques and methodologies deployed to tackle this complex optimization problem. Furthermore, we will delve into the intricate and multifaceted stochastic and dynamic constraints that pose formidable obstacles to effective route planning and optimization. Through this survey paper, we seek to provide a comprehensive understanding of the current state-of-the-art approaches and highlight the potential avenues for future research in this critical area of transportation logistics. In addition, we will also analyze the application of advanced reinforcement learning methods and Markov decision processes to solve the problem of stochastic dynamic vehicle routing.