Best Paper Award for 2017

Qing Chuan Ye and Yingqian Zhang received a “Best Paper Award for 2017” from Omega-International Journal of Management Science.

The award-winning paper “Fair task allocation in transportation”, co-authored with Rommert Dekker, is available at

The paper studies a max-min fair, minimum-cost task allocation problem in transportation. It proposes an efficient algorithm for allocating tasks and resources in a fair way among players. The experiments show that often fairness comes with a very small price in terms of cost. In many cases such as applications in Sharing Economy, such a fair allocation algorithm is more socially desired than the classical cost minimization ones.


New Employee Reza Refaei Afshar






I received my Bachelor’s degree from Ferdowsi University of Mashhad in 2012 and then, my Master’s degree from University of Tehran in 2015. After that I worked for 2 years as a data scientists in Tehran.

Now, I am a PhD student at TU/e in Information Systems (IS) group. My research focus is on the programmatic advertising decision system project which involves collaboration with high tech industry. My research interests also include data mining, data analysis, machine learning and social networks analysis.

New Employee Paulo De Oliveira Da Costa






I obtained my Bachelor’s degree in Computational and Applied Mathematics in 2010 from the University of Campinas (Unicamp), with a specialisation in Operational Research. After my bachelor studies, I worked in Data Analytics roles for 4.5 years at two major companies in the banking sector, based in São Paulo, Brazil.

I then moved to Dublin, Ireland to pursue a Master’s degree in Business Analytics at the University College Dublin (UCD). After completing the programme in 2016, I extended my stay in Ireland working as Data Scientist.

At TU/e I will work within the Information Systems (IS) and the Operations, Planning, Accounting and Control (OPAC) groups as PhD student on the Real-time data-driven maintenance logistics project (WP1), which aims to leverage dynamic maintenance logistics policies supported by real-time data. The topic is focused on the integration of machine learning and optimisation models for more efficient and real-time decision making. I am excited to start working on the topic and looking forward to the next years of learning and collaboration.