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- W4280613146 abstract "In this article, a new deep Reinforcement Learning (RL) model is proposed to solve the Single Container Loading Problem (SCLP) as well as the SCLP with full support. For that purpose, a multilayer neural network architecture is used. Computational experiments, conducted on benchmark instances with homogeneous boxes, revealed that the proposed model yields fairly good results compared to those found with state-of-the-art heuristics. Experiments have also shown good generalization capability of the proposed deep RL model to deal with both homogeneous and heterogeneous classes of instances. Nevertheless, this learning-based optimization approach still has an optimality gap compared to the well-designed heuristics of the operations research literature. Finally, the benefit of training the model under different levels of variability has been analysed. Results revealed that, for better performance, and if a company does not face high demand volatility, it is recommended to train the model under a low level of variability." @default.
- W4280613146 created "2022-05-22" @default.
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- W4280613146 date "2022-05-11" @default.
- W4280613146 modified "2023-10-16" @default.
- W4280613146 title "Deep reinforcement learning for solving the single container loading problem" @default.
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- W4280613146 doi "https://doi.org/10.1080/0305215x.2021.2024177" @default.
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