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School of Industrial Engineering and Management

Our core knowledge areas include industrial design and innovation, product and production development, materials development, energy technology, learning in engineering sciences as well as industrial economics, organisation and management. Our efforts are directed towards a green and sustainable society for the future.

Departments 

Campus relocation

KTH has decided to relocate the educational programmes and teaching staff at the Södertälje campus to the KTH campus and KTH Flemingsberg. The relocation will be gradual and will begin in the spring semester of 2025 for parts of the operations. From 2027, KTH will conduct its remaining activities and collaboration with strategic partners such as Scania and Astra-Zeneca in new forms.

More about the campus relocation for students

More about the campus relocation for employees

Latest news

Photo by Bernd Dittrich on Unsplash

The impact of Electrification on Road Freight Transport Efficiency: a PhD presentation

On 30th September, Claudia Andruetto and Zeinab Raoofi presented their recent work titled “How Does Electrification Impact Road Freight Transport Efficiency? A System Dynamics Approach” at the ITRL’s ...

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students on study visit
Photo: Scania.

Successful week long study visit shows close collaboration with industry

During four intense weeks, the around 100 new students in the programme Industrial Technology (Industriell teknik), got the opportunity to meet companies and their employees, go on field trips, and le...

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TADDO2 presenters at SafeComp2024
TADDO2 presenters at SafeComp2024

Taddo2: collaborative presentation at Safecomp 2024 in Florence!

Reconciling software and machine learning practices with safety engineering!

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Calendar

Publications

[1]
Z. Zhou et al., "Learning accurate and efficient three-finger grasp generation in clutters with an auto-annotated large-scale dataset," Robotics and Computer-Integrated Manufacturing, vol. 91, 2025.
[2]
M. Sun et al., "Out-of-order execution enabled deep reinforcement learning for dynamic additive manufacturing scheduling," Robotics and Computer-Integrated Manufacturing, vol. 91, 2025.
[3]
Y. Qin et al., "A tool wear monitoring method based on data-driven and physical output," Robotics and Computer-Integrated Manufacturing, vol. 91, 2025.
Full list in the KTH publications portal