1 code implementation • 28 May 2024 • Zangir Iklassov, Yali Du, Farkhad Akimov, Martin Takac
We present our research as the first to apply LLMs to a broad range of CPs and demonstrate that SGE outperforms existing prompting strategies by over 27. 84% in CP optimization performance.
no code implementations • 15 Feb 2024 • Zangir Iklassov, Ikboljon Sobirov, Ruben Solozabal, Martin Takac
This paper introduces a reinforcement learning approach to optimize the Stochastic Vehicle Routing Problem with Time Windows (SVRP), focusing on reducing travel costs in goods delivery.
1 code implementation • 13 Nov 2023 • Zangir Iklassov, Ikboljon Sobirov, Ruben Solozabal, Martin Takac
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.
1 code implementation • 9 Jun 2022 • Zangir Iklassov, Dmitrii Medvedev, Ruben Solozabal, Martin Takac
Current models on the JSP do not focus on generalization, although, as we show in this work, this is key to learning better heuristics on the problem.
1 code implementation • 26 May 2022 • Zangir Iklassov, Dmitrii Medvedev, Otabek Nazarov, Shakhboz Razzokov
With the development of deep learning, scientists actively started working on developing machine-learning-based approaches to identify physical properties without any manual experiments.
no code implementations • 25 May 2022 • Zangir Iklassov, Dmitrii Medvedev
Keywords: Graph Neural Network (GNN), Logistics optimization, Reinforcement Learning