Context :

At Stanley Robotics, we build a robotized parking solution that automatically stores cars, using a fleet of robots.
In order to increase the efficiency of our solution, we want to optimize the algorithms of the Fleet Management System (FMS), the service in charge of allocating tasks to robots.

Your mission :

In our system, missions are created on the flow to store and retrieve vehicles, and these missions are allocated each time a robot is available, using matching algorithms.
We propose with this internship to explore new approaches, in particular build a complete schedule of all available missions instead of single task allocation, and quantify the performance improvements and losses. 

Your mission will be to model this new problem, to design an algorithm to build a complete schedule for each robot, and adapt it in real-time to operational events. Motivation of such algorithms is the optimisation of resources use and reduction of customer delays. You will validate your algorithms using simulation and real data.

You will join our R&D team of experienced developers from various backgrounds, in a challenging, creative and friendly atmosphere.

Your profile :

• You are in your last year of engineering school - min 6 months internship, specialized in applied mathematics / operations research

• Good algorithmic skills

• Good programming skills in Python and/or Go is a plus

• Knowledge about metaheuristic algorithms and robustness theory 

• Be able to work autonomously on challenging tasks

• French and English at a professional level