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Given the current scenario of uncertainty in port operations, the berth allocation problem remains a crucial issue in port operations. This work presents a method that combines the Deep Q-Network (DQN) algorithm with the Long Short-Term Memory (LSTM) neural network architecture within a reinforcement learning framework applied to berth allocation under inventory constraints. The results are promising and indicate that the approach can respect inventory limits while producing quality solutions comparable to those obtained by a commercial solver, thereby offering support for informed decision-making.

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Deep Reinforcement Learning for the Berth Allocation Problem with Inventory Control: A DQN-LSTM Approach

  • 会议论文

  • 发表日期：

    2026年

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  • Victor Hugo Barros,
  • Alexandre César Muniz de Oliveira

摘要

Given the current scenario of uncertainty in port operations, the berth allocation problem remains a crucial issue in port operations. This work presents a method that combines the Deep Q-Network (DQN) algorithm with the Long Short-Term Memory (LSTM) neural network architecture within a reinforcement learning framework applied to berth allocation under inventory constraints. The results are promising and indicate that the approach can respect inventory limits while producing quality solutions comparable to those obtained by a commercial solver, thereby offering support for informed decision-making.