arXiv Machine Learning By Liangyu Ding, Guokai Li, Zizhuo Wang, Chenghan Wu

Self-Improving Neural Pruning: A Graph Neural Network Framework for Scalable Mixed Bundle Pricing

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arXiv:2509. 22557v3 Announce Type: replace Abstract: Mixed bundle pricing is a classic revenue management problem arising in industries such as e-commerce, tourism, and video games.

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arXiv Machine Learning
Sep 23

Deep Reinforcement Learning on Item-Compatibility Graphs for One-Dimensional Bin Packing

The paper introduces a novel end‑to‑end, size‑agnostic graph reinforcement learning framework for the one‑dimensional bin packing problem (1D‑BPP). It models packing as a Markov decision process on an item‑compatibility graph, where a graph neural network actor‑critic policy learns to merge compatible partial bins. Empirical results on the BPPLIB benchmark show that the learned policy reduces the mean optimality gap of a constructive heuristic from 2.66 % to 2.31 %, performs competitively against other learned methods, and outperforms a state‑of‑the‑art learned solver on the hardest benchmark family.

By M. Asl{\i} Ayd{\i}n