Scientific advisor at Dutch Railways (NS)

Last week I started at the Dutch Railways in Bob Huisman’s group on Maintenance Research and Development as a scientific advisor, for one day a week. My aim is to support and further increase the exchange of knowledge and experience between Dutch Railways and Delft University of Technology. I aim to contribute new scientific developments in the area of algorithm design and artificial intelligence to this practical scheduling problem, but also to learn about important research questions regarding real-life problems. Concretely, I’m advising master and PhD students doing an internship with NS on the development of algorithms for shunting and service/maintenance logistics. The research group at NS consists of an inspired bunch of master students, PhD students and some tenure staff, all with strong ties to Utrecht, Erasmus, Eindhoven, Twente, or, of course, Delft University. I immediately felt at home.

These first two days consisted mainly of reading, listening, and learning. My expectations were more than just confirmed: this practical problem is significantly more complex than my typical benchmark problem. In fact this shunting problem can be seen as a combination of five (!) more basic/general problems such as matching, flow shop, path finding, etc.. In addition there are many “dirty details” (exceptions, security-related conditions, etc.), real data is incomplete, the realised situation may actually be different from the problem input, and using all of this effectively will require a significant change in the organisation. I like a good challenge!

The current algorithmic state of the art is a very smart local search approach by Roel van den Broek which he presented last Wednesday at the International Conference on Operations Research in Berlin. I have two concrete ideas to continue from this: I’d like to improve the quality of the results from the local search by embedding it in a branch-and-bound algorithm, and I’d like to get result that are a bit more robust to changes by applying ideas from robust and stochastic optimisation, while still using the local search procedure. I’m happy I also found a good student who wants to work with me, because this may be a but more work than just one day a week. Contact me or stay tuned for more.

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