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<p>The one-dimensional Bin Packing Problem (1D-BPP) poses significant challenges in logistics, particularly for optimizing item packing sequences in warehouse conveyor belt systems. This paper proposes a Deep Reinforcement Learning (DRL) approach using Advantage Actor-Critic (A2C) integrated with Pointer Networks to learn optimized item sequencing policies for 1D-BPP. Our method generates item permutations that are subsequently processed by <span>First-Fit</span>, <span>Next-Fit</span>, or <span>Best-Fit</span> heuristics, effectively bridging the gap between offline sequence optimization and online packing constraints. The Actor Network, incorporating a Pointer Network with an LSTM-based encoder-decoder and attention mechanism, learns a stochastic policy to produce permutations that improve bin occupancy. The Critic Network estimates the state-value function to reduce variance during policy gradient updates. Experimental results on small (5–10 items), medium (20–30 items), and large (100–200 items) problem instances demonstrate that DRL-enhanced <span>First-Fit</span> and <span>Best-Fit</span> achieve statistically significant improvements in average bin occupancy over standalone heuristics and <span>First-Fit-Decreasing</span> baselines (<InlineEquation ID="IEq1"><EquationSource Format="TEX">\(p &lt; 0.001\)</EquationSource></InlineEquation>), with effect sizes ranging from moderate to very large. The proposed framework offers a computationally efficient approach to sequence optimization for bin packing in dynamic warehouse environments.</p>

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Optimizing one-dimensional bin packing for conveyor belt logistics via pointer-network-based A2C and heuristics

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月3日

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  • Xingwang Huang,
  • Xingkai Wang,
  • Yunming Pu,
  • Chaopeng Li,
  • Weiquan Liu

摘要

The one-dimensional Bin Packing Problem (1D-BPP) poses significant challenges in logistics, particularly for optimizing item packing sequences in warehouse conveyor belt systems. This paper proposes a Deep Reinforcement Learning (DRL) approach using Advantage Actor-Critic (A2C) integrated with Pointer Networks to learn optimized item sequencing policies for 1D-BPP. Our method generates item permutations that are subsequently processed by First-Fit, Next-Fit, or Best-Fit heuristics, effectively bridging the gap between offline sequence optimization and online packing constraints. The Actor Network, incorporating a Pointer Network with an LSTM-based encoder-decoder and attention mechanism, learns a stochastic policy to produce permutations that improve bin occupancy. The Critic Network estimates the state-value function to reduce variance during policy gradient updates. Experimental results on small (5–10 items), medium (20–30 items), and large (100–200 items) problem instances demonstrate that DRL-enhanced First-Fit and Best-Fit achieve statistically significant improvements in average bin occupancy over standalone heuristics and First-Fit-Decreasing baselines (\(p < 0.001\)), with effect sizes ranging from moderate to very large. The proposed framework offers a computationally efficient approach to sequence optimization for bin packing in dynamic warehouse environments.