arXiv AI

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.

arXiv AI

2d ago

Reinforcement Learning-Based Production Scheduling in an Industry-Based Coating Scenario Using the Digital Model Playground

arXiv:2608. 14122v1 Announce Type: new Abstract: Production scheduling in complex manufacturing environments is challenging when sequence-dependent setup times, stochastic disturbances, and due-date constraints must be addressed simultaneously.

By Arne Kr\"oger, Ralf Buscherm\"ohle, Wilhelm Hasselbring, Henrik Wilbers

agents reinforcement-learning benchmarks

arXiv AI

Aug 5

PLAN: Parallel Liquid-Inspired Approximation Network for Efficient Representation Learning in Flexible Job Shop Scheduling

arXiv:2608. 03041v1 Announce Type: cross Abstract: Deep reinforcement learning (DRL) approaches for flexible job shop scheduling (FJSP) heavily rely on attention-centric architectures to achieve state-of-the-art performance.

By Dhivya Dharshini Kannan, Wei Zhang, Jieyi Bi, Yingpeng Du, Tianjun Wei, Jie Zhang, Zuming Liu, Anupam Trivedi

llms reinforcement-learning efficiency benchmarks

arXiv AI

Jun 10

Event-Driven Reinforcement Learning Enables Long-Horizon Control in Semiconductor Fabrication

arXiv:2606. 10705v1 Announce Type: cross Abstract: Reinforcement learning promises to optimize sequential decisions in large-scale systems.

By Yavar Yeganeh, Mahsa Shekari, Nicla Frigerio, Daniele Pagano, Andrea Matta

agents reinforcement-learning efficiency

arXiv AI

Jun 15

Scalable Production Scheduling: Linear Complexity via Unified Homogeneous Graphs

arXiv:2604. 23841v2 Announce Type: replace-cross Abstract: Efficiently solving the Job Shop Scheduling Problem in real-world industrial applications requires policies that are both computationally lean and topologically robust.

By Jonathan Hoss, Moritz Link, Noah Klarmann

agents diffusion reinforcement-learning benchmarks

arXiv AI

Jul 14

Time-Lag-Aware Deep Reinforcement Learning for Flexible Job-Shop Scheduling in PPVC Module Factories

arXiv:2607. 11725v1 Announce Type: cross Abstract: Prefabricated prefinished volumetric construction moves most building work into module factories, whose production floor operates as a flexible job shop.

By Ziheng Zhang, Wei Zhang

rag reinforcement-learning benchmarks

arXiv Machine Learning

Jul 7

A Policy Decomposition Framework for Dynamic Order Fulfillment Operations

arXiv:2607. 04056v1 Announce Type: cross Abstract: Modern supply chains span diverse operational environments, ranging from e-commerce distribution networks to customized production-to-order manufacturing lines.

By Gal Neria, Michal Tzur, Marlin W. Ulmer

reinforcement-learning benchmarks

arXiv AI

Jul 23

Coordinating from Memory: Graph-Structured Experience Reuse for Multi-Agent Adaptation in Dynamic Manufacturing

arXiv:2607. 19985v1 Announce Type: new Abstract: Dynamic manufacturing environments require multi-agent systems to coordinate effectively under frequent operational disturbances such as machine failures, urgent job arrivals, and processing time variations.

By Chengxiao Dai, Zhanhui Lin, Zhaokun Yan, Youyang Ni, Chenjun Lei, Luyan Zhang

agents reinforcement-learning benchmarks

arXiv AI

Jun 6

Learning to replenish: A hybrid deep reinforcement learning for dynamic inventory management in the pharmaceutical supply chains

arXiv:2606. 06201v1 Announce Type: new Abstract: Pharmaceutical supply chains (PSCs) struggle with inventory management (IM) due to unpredictable demand patterns and variable lead times associated with restocking.

By Amandeep Kaur, Gyan Prakash

reinforcement-learning benchmarks

arXiv AI

Jun 18

Maturing Markov Decision Processes: Decision Making under Increasing Information and Shrinking Action Sets

arXiv:2606. 18820v1 Announce Type: cross Abstract: Sequential decision problems often exhibit an asymmetric evolution of information and decision flexibility: as a decision cycle unfolds, the agent receives richer information while feasible actions expire due to operational cutoffs, commitments, or resource constraints.

By Jiaxi Liu, Aiping Yang, Yuhang Yang, Shuqi Zhang, Zewei Dong, Jiangming Yang, Xuebin Chen

agents reinforcement-learning efficiency

arXiv AI

Jun 15

An Analysis of the Coordination Gap between Joint and Modular Learning for Job Shop Scheduling with Transportation Resources

arXiv:2604. 24117v2 Announce Type: replace Abstract: Efficient job-shop scheduling with transportation resources is critical for high-performance manufacturing.

By Moritz Link, Jonathan Hoss, Noah Klarmann

agents reinforcement-learning

arXiv Machine Learning

Jul 23

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.

By Lei Gao, Shihong Huang, Shengjie Wang, Hong Ma, Feng Zhang, Hengda Bao, Qichang Chen, Weihua Zhou

reinforcement-learning

arXiv Machine Learning

Jun 24

A Comparative Study of Bayesian Contextual Bandits for Real-Time Warehouse Sorter Optimization

arXiv:2606. 23977v1 Announce Type: new Abstract: Efficient sorter diversion control of automated material handling systems (MHS) is critical for optimizing operational efficiency in large-scale warehouse environments.

By Tina Dongxu Li, Mouhacine Benosman, Ken Meszaros, Trevor Dardik

reinforcement-learning efficiency

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