arXiv AI July 29, 2026 By XinLei Zhou, Jin Huang, Jie Yang, Xinyu Li, Liang Gao

DSevolve: Enabling Real-Time Adaptive Scheduling on Dynamic Flexible Job Shop with LLM-Evolved Heuristic Portfolios

arXiv:2603. 27628v2 Announce Type: replace Abstract: In dynamic flexible job shops, order arrivals, machine breakdowns, and processing-time deviations continually reshape the scheduling state and the priority trade-offs behind dispatching decisions.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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 AI

Jun 15

A Deep Reinforcement Learning (DRL)-Based Transformer Method for Solving the Open Shop Scheduling Problem

arXiv:2606. 13682v1 Announce Type: new Abstract: The open shop scheduling problem (OSSP) arises in many industrial and service settings but remains computationally challenging as the number of jobs and machines increases.

By Faezeh Ardali, Mwembezi A. Nyelele, Gerald M. Knapp

llms reinforcement-learning benchmarks

arXiv AI

Jun 11

Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions

arXiv:2509. 10303v2 Announce Type: replace-cross Abstract: Online reinforcement learning (RL) approaches have demonstrated strong performance on Job Shop Scheduling (JSP) and Flexible JSP (FJSP) problems by learning scheduling policies through direct interaction with simulated environments.

By Jesse van Remmerden, Zaharah Bukhsh, Yingqian Zhang

reinforcement-learning benchmarks

arXiv AI

Jun 2

Deft Scheduling of Dynamic Cloud Workflows with Varying Deadlines via Mixture-of-Experts

arXiv:2606. 01162v1 Announce Type: new Abstract: Workflow scheduling in cloud computing demands the intelligent allocation of dynamically arriving, graph-structured workflows with varying deadlines onto ever-changing virtual machine resources.

By Ya Shen, Gang Chen, Hui Ma, Mengjie Zhang

reinforcement-learning benchmarks

arXiv AI

Jun 18

ProfiLLM: Utility-Aligned Agentic User Profiling for Industrial Ride-Hailing Dispatch

arXiv:2606. 18803v1 Announce Type: new Abstract: Bringing Large Language Models (LLMs) into industrial ride-hailing dispatch as semantic feature extractors over platform-scale behavioral logs is a compelling but under-explored data systems problem.

By Tengfei Lyu, Zirui Yuan, Xu Liu, Kai Wan, Zihao Lu, Li Ma, Hao Liu

llms agents reinforcement-learning fine-tuning data-engineering

arXiv AI

Jul 7

Adaptive Inference Batching using Policy Gradients

arXiv:2607. 05272v1 Announce Type: cross Abstract: Inference serving systems must balance throughput and latency under bursty, heterogeneous workloads, yet the industry standard remains static batching policies that require manual tuning and cannot adapt to shifting traffic.

By Ruslan Sharifullin

agents reinforcement-learning efficiency

Related stories

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

A Deep Reinforcement Learning (DRL)-Based Transformer Method for Solving the Open Shop Scheduling Problem

Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions

Deft Scheduling of Dynamic Cloud Workflows with Varying Deadlines via Mixture-of-Experts

ProfiLLM: Utility-Aligned Agentic User Profiling for Industrial Ride-Hailing Dispatch

Adaptive Inference Batching using Policy Gradients