Internship | Improving productivity analysis for Cyber-Physical Production Systems

LSAT uses models that abstract low-level, functionally deterministic behavior, like the move of a robot arm or a turn table, into high-level activities. Using automata, all possible activity sequences can be modeled. LSAT provides various analysis techniques to compute a schedule with optimal productivity from these models.
By using abstract models, LSAT allows designers to effectively explore the system timing behavior without having to resort to detailed models and time-consuming analysis of the system’s mechanics and embedded control.

The system timing behavior is analyzed using the mathematical framework of max-plus algebra. Max-plus algebra is an algebra in which the max and plus operations are defined as addition and multiplication in conventional algebra. The max operation is used to model synchronization, where an action can only start if some preceeding actions have finished their execution. The plus operation is used to model execution times of actions. We use max-plus matrices to capture the timing behavior of activities. The precise timing of activity execution depends on interactions between activities. The timing behavior of activity sequences is computed by repeated max-plus matrix multiplication. Such sequences are used to describe the logistics in the system that ensure a correct product flow.

This assignment focusses on improving the max-plus performance analysis in LSAT. In some models, we encounter situations that yield an infinite state space in the analysis. This is often caused by system resources that do not synchronize at any point in time. At the moment, such situation can be detected, but optimal productivity analysis is not yet possible. The goal of this assignment is to develop new analysis techniques that can deal with these situations.

The assignment will be carried out at TNO's Embedded Systems Innovation department at the High Tech Campus in Eindhoven in cooperation with Nexperia.

You are a Master student in Computer Science, Embedded Systems, Electrical Engineering or Mathematics looking for a graduation assignment of 6-9 months. You are interested in performance aspects of cyber-physical production systems. You have relevant background knowledge in the area of formal methods, synchronous data flow, and/or max-plus algebra. You are excited about developing new knowledge and contributing to an applied research project in collaboration with industry.

You want to work on the precursor of your career; a work placement gives you an opportunity to take a good look at your prospective future employer. TNO goes a step further. It’s not just looking that interests us; you and your knowledge are essential to our innovation. That’s why we attach a great deal of value to your personal and professional development. You will, of course, be properly supervised during your work placement and be given the scope for you to get the best out of yourself. Naturally, we provide suitable work placement compensation.

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