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arXiv Machine Learning

One4Many-StablePacker: An Efficient Deep Reinforcement Learning Framework for the 3D Bin Packing Problem

arXiv:2510. 10057v2 Announce Type: replace Abstract: The three-dimensional bin packing problem (3D-BPP) is widely applied in logistics and warehousing.

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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
reinforcement-learningbenchmarks
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arXiv Machine Learning
Sep 3

OR-Transformer: Scaling Real-Time Decision-Making to 1,000 Items

OR-Transformer is a deep reinforcement learning framework designed for joint replenishment in supply chain operations with thousands of items. It uses a permutation‑equivariant Transformer architecture and pathwise‑gradient training to handle high‑dimensional observation and action spaces. In tests up to 1,024 items, it outperforms both learning‑based and rolling‑horizon MILP baselines and cuts online decision time by over four million times.

By Shuze Daniel Liu, David Simchi-Levi, Claire Chen, Chutong Gao, Shangtong Zhang
llmsreinforcement-learning
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arXiv AI
Jul 7

A Sliding-Window-Based Reinforcement Learning for Dynamic Assembly Flow Shop Scheduling with Multi-Product Delivery

arXiv:2607. 02941v1 Announce Type: new Abstract: Multi-product kitting delivery imposes significant challenges for real-time scheduling in hybrid manufacturing systems that integrate processing and assembly, as dynamic order arrivals simultaneously alter supply dependencies and the set of feasible job-machine assignments.

By Junhao Qiu, Jianjun Liu, Ting Liu, Rongjie Liao, Zhantao Li, Qingfu Zhang
reinforcement-learning
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arXiv AI
Jun 3

ASAP: Exploiting the Satisficing Generalization Edge in Neural Combinatorial Optimization

arXiv:2501. 17377v4 Announce Type: replace-cross Abstract: Deep Reinforcement Learning (DRL) has emerged as a promising approach for solving Combinatorial Optimization (CO) problems, such as the 3D Bin Packing Problem (3D-BPP), Traveling Salesman Problem (TSP), or Vehicle Routing Problem (VRP), but these neural solvers often exhibit brittleness when facing distribution shifts.

By Han Fang, Paul Weng, Yutong Ban
reinforcement-learningfine-tuningbenchmarks
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arXiv AI
Jun 12

Multi-Agent Reinforcement Learning from Delayed Marketplace Feedback for Objective-Weight Adaptation in Three-Sided Dispatch

arXiv:2606. 13604v1 Announce Type: new Abstract: Dispatch in three-sided marketplaces provides a natural setting for reinforcement learning from world feedback: decisions are evaluated by delayed operational outcomes such as delivery speed, courier utilization, and merchant congestion.

By Haochen Wu, Yi Hou, Shiguang Xie
agentsreinforcement-learning
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