Methodological Contribution & Project Framework
DeepScheduling Framework: Semantic Modelling, Explainable AI, and Low-CAPEX Decarbonisation via SEEP 2026 1. Executive Summary At the SEEP 2026 Conference (Tor Vergata University, Rome), the DeepScheduling consortium presented its core methodological contribution:...
Technical State-of-the-Art & Problem Formulation: Energy-Aware Production Scheduling in Steel Heat Treatment: Mapping the Literature, Operational Drivers, and the Research Gap
1. Executive Summary Heat treatment of steel is an essential, energy-intensive metallurgical phase designed to grant critical mechanical and structural properties to high-performance steel products. While significant academic and industrial attention has historically...
The knowledge missing from algorithms. DeepScheduling puts the operator at the centre.
The steel industry has been optimising its heat treatment furnaces since the 1930s. Ninety years of research into temperature profiles, heat balances, combustion models and linear programming algorithms. And in all this time, no review article has operationally...
DeepScheduling and Production Flexibility: AI to Address Supply Chain Disruptions
On February 28, 2026, U.S. and Israeli airstrikes on Iran triggered an unprecedented disruption in global energy supplies and prices, according to the International Energy Agency. On March 2, Iran declared the Strait of Hormuz closed, the maritime passage through...
Advancing AI-Based Scheduling: DeepScheduling Meeting in Düsseldorf
The DeepScheduling partners gathered in Düsseldorf for a project meeting hosted by BFI, focusing on the advancement of AI-driven scheduling solutions for steel heat treatment processes. During the meeting, partners presented recent progress and discussed next steps in...
The European AI Act requires companies to explain their industrial AI systems. DeepScheduling already does this
On August 2, 2026, the European Union’s Artificial Intelligence Regulation, the world’s first comprehensive AI legislation, will come into full effect. While many European industrial companies are under pressure to adapt their AI systems to the new regulatory...
DeepScheduling in the Context of the Steel Trade War: Trump’s Tariffs Make Efficiency a Matter of Survival.
Amid unprecedented trade pressure, with U.S. tariffs of 50% on European steel and exports to the American market plummeting by 30% in just a few months, our project, DeepScheduling, shows that one of the few variables the European steel industry can control is its...
DeepScheduling: a technological response to the gas crisis caused by the conflict in Iran?
In a context of intense geopolitical tension where the price of natural gas has risen by 44% due to the conflict in Iran, the DeepScheduling project positions itself as a key tool for economic resilience. DeepScheduling aims to validate an artificial...
Toward a Common Language for Hybrid Temporal Scheduling
DeepScheduling is progressing toward the definition of a Common Modeling Language for Hybrid Temporal Scheduling (CML4HTS), a key step in enabling interoperable, solver-agnostic scheduling solutions for complex industrial environments. The proposed approach introduces...