arXiv Machine Learning October 19, 2026 By Ziheng Zhang, Wei Zhang, Yunyi Zhao, Zuming Liu, Wei Zhou, Boon Ping Gan

BOLT: Multi-Bottleneck Bound-Guided Learning for Flexible Job-Shop Scheduling with Time Lags and Resource-Coupled Transport

Read the original on arXiv Machine Learning →

The Flow has not summarised this story yet — read it at arXiv Machine Learning.

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

Oct 16

Imperfect Is Perfect: Learned Column Prefilling for Branch-Price-and-Cut

arXiv:2610. 21001v1 Announce Type: new Abstract: Column generation folklore warns that initial columns hurt: a warm start may bias the dual and slow the root of branch-price-and-cut.

By Haoran Liu, Yu Yang

benchmarks safety

arXiv AI

Jul 21

Certified-Gap Dual-Price Policies for Real-Time Truckload Bid Acceptance with Relocating, Clock-Constrained Resources

arXiv:2607. 16891v1 Announce Type: cross Abstract: A truckload carrier must accept or reject each load tender within seconds.

By Aswin Chandrasekaran

benchmarks

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 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

Jul 9

Latency-Aware Bid Acceptance under Operational Feasibility: A Public Benchmark with Hindsight Ceilings

arXiv:2607. 07343v1 Announce Type: cross Abstract: Online truckload bid acceptance is a closed-loop stochastic decision problem in which a carrier or broker must, in real time, accept or reject a tendered load subject to operational feasibility, fleet repositioning costs, and opportunity cost against future demand.

By Aswin Chandrasekaran

benchmarks

One email a morning, machine-written

Related stories

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

Imperfect Is Perfect: Learned Column Prefilling for Branch-Price-and-Cut

Certified-Gap Dual-Price Policies for Real-Time Truckload Bid Acceptance with Relocating, Clock-Constrained Resources

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

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

Latency-Aware Bid Acceptance under Operational Feasibility: A Public Benchmark with Hindsight Ceilings