arXiv AI June 17, 2026 By Carlos Eduardo Sanoja

Skill-Constrained Model Predictive Control for Resilient Manufacturing Supply Chains

arXiv:2606. 17269v1 Announce Type: new Abstract: In skill-constrained production-inventory systems, the qualified human capacity available tomorrow depends on training decisions made today: production requires certified workers, certifications decay unless maintained, and training consumes the same scarce worker hours that production needs now.

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

arXiv AI

Jun 17

SkillChain-Gym: A Benchmark for Reskilling-Aware Production-Inventory Control under Disruptions

arXiv:2606. 17266v1 Announce Type: new Abstract: Production planning increasingly has to treat workforce capability as a decision variable: certifications lapse when skills are not maintained, new products require skills the current workforce does not hold, and reskilling competes for the same worker hours needed for production.

By Carlos Eduardo Sanoja

benchmarks

arXiv AI

Jul 1

Qualified Educational Capacity Planning under Heterogeneous Student Support Needs: A Synthetic Benchmark and Decision-Support Framework

arXiv:2606. 30650v1 Announce Type: cross Abstract: Educational support services often face a qualified-capacity problem: staff time is scarce, qualifications decay, new support needs can appear before anyone is prepared for them, and training consumes the same hours needed by current students.

By Carlos Eduardo Sanoja, Oscar Enrique Moreno Mayz

benchmarks safety

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 31

Real-Time Hard Peak Age-of-Information Safety with No-Regret Learning

arXiv:2607. 27626v1 Announce Type: new Abstract: Safety-critical IoT systems such as industrial closed-loop control, V2X coordination, and remote teleoperation require every sensor's peak Age of Information (peak AoI, also abbreviated PAoI) to stay below a hard per-slot deadline, not merely an average bound.

By Wentao Zhang, Wentao Mo

reinforcement-learning safety

arXiv Machine Learning

Jul 14

A Control Theory of Predictability in Latent World Models

arXiv:2607. 10362v1 Announce Type: new Abstract: Latent world models are trained to predict future states in a learned representation and are then deployed inside a planner that selects actions by simulating them forward.

By Hanzhe You, Yonggang Zhang, Maohao Ran, Zhiqin Yang, Zhenyuan Zhang, Wei Xue, Jun Song, Xinmei Tian, Yike Guo

arXiv Machine Learning

Aug 4

Hard Constraints, Smooth Gradients: Learning Feasible Inventory Policies via Differentiable Projection

arXiv:2608. 02343v1 Announce Type: cross Abstract: Many operational problems are constrained sequential decision processes with large, combinatorial action spaces and interdependent feasibility constraints.

By Patrick Helm, Jan-Niklas Doerr, Joren Gijsbrechts, Stefan Minner

rag reinforcement-learning benchmarks

Related stories

SkillChain-Gym: A Benchmark for Reskilling-Aware Production-Inventory Control under Disruptions

Qualified Educational Capacity Planning under Heterogeneous Student Support Needs: A Synthetic Benchmark and Decision-Support Framework

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

Real-Time Hard Peak Age-of-Information Safety with No-Regret Learning

A Control Theory of Predictability in Latent World Models

Hard Constraints, Smooth Gradients: Learning Feasible Inventory Policies via Differentiable Projection