Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Iklassov, Zangir"'
Large Language Models (LLMs) have become pivotal in addressing reasoning tasks across diverse domains, including arithmetic, commonsense, and symbolic reasoning. They utilize prompting techniques such as Exploration-of-Thought, Decomposition, and Ref
Externí odkaz:
http://arxiv.org/abs/2405.17950
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. We develop a novel SVRP formulation that accounts for uncertain
Externí odkaz:
http://arxiv.org/abs/2402.09765
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
Externí odkaz:
http://arxiv.org/abs/2311.07708
This paper introduces a Reinforcement Learning approach to better generalize heuristic dispatching rules on the Job-shop Scheduling Problem (JSP). Current models on the JSP do not focus on generalization, although, as we show in this work, this is ke
Externí odkaz:
http://arxiv.org/abs/2206.04423
Geologic cores are rock samples that are extracted from deep under the ground during the well drilling process. They are used for petroleum reservoirs' performance characterization. Traditionally, physical studies of cores are carried out by the mean
Externí odkaz:
http://arxiv.org/abs/2205.13189
Autor:
Iklassov, Zangir, Medvedev, Dmitrii
Logistics optimization nowadays is becoming one of the hottest areas in the AI community. In the past year, significant advancements in the domain were achieved by representing the problem in a form of graph. Another promising area of research was to
Externí odkaz:
http://arxiv.org/abs/2205.12888
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