An average day…

You will work in the Product Quality & Innovation (PQI) Department, together with researchers from different technical backgrounds. Your work will focus on improving the use of hot rooms in slab logistics at Tata Steel Nederland. The goal is to support better allocation decisions that keep slabs hot for longer, while avoiding unnecessary crane operations and reshuffling.

You will build on an existing slab logistics model and previously developed heuristic approaches. Your main task will be to investigate how AI methods can support smarter hot-room allocation and help evaluate future logistics concepts.

What are you going to do?

Steel slabs lose thermal energy during storage and transport between production areas and the hot strip mill. Hot rooms can reduce this heat loss, but their value depends strongly on how they are used. Poor allocation may lead to unnecessary slab movements, extra crane operations, and limited operational acceptance.

In this internship, you will develop and evaluate an AI-based decision-support approach for hot-room allocation. The preferred direction is a hybrid AI method, where existing heuristic or meta-heuristic solutions are used to train an initial decision model, which may later be improved using reinforcement learning or related techniques.

The model should help answer questions such as:

• how to increase hot-room utilization without excessive crane movements;
• whether additional or mobile hot rooms provide operational value;
• whether heated hot rooms could further improve energy efficiency.

The work will be performed using simulation scenarios and available process/logistics data where possible. The result should be a compact prototype and evaluation showing how AI can support future energy-efficient slab logistics.