Former Doctoral Candidate
Jason Rhuggenaath
Department / Institute
Industrial Engineering and Innovation Sciences
RESEARCH PROFILE
Jason Rhuggenaath received his MSc. degree in Management Science and Operations Research from Erasmus University Rotterdam where he also obtained his MSc. degree in Economics and Business Economics. Currently, he pursues a Ph.D. at the School of Industrial Engineering in the Information Systems group. His research interests are data-driven optimization, sequential decision-making under uncertainty and machine learning, focusing on applications in operations management and revenue management.
Recent Publications
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Jason Rhuggenaath,Alp Akcay,Yingqian Zhang,Uzay Kaymak
Setting reserve prices in second-price auctions with unobserved bids
INFORMS Journal on Computing (2022) -
Reza Refaei Afshar,Jason Rhuggenaath,Yingqian Zhang,Uzay Kaymak
A Reward Shaping Approach for Reserve Price Optimization using Deep Reinforcement Learning
2021 International Joint Conference on Neural Networks, IJCNN 2021 (2021) -
Paulo R. de O. da Costa,Jason Rhuggenaath,Yingqian Zhang,Alp Akcay,Uzay Kaymak
Learning 2-Opt Heuristics for Routing Problems via Deep Reinforcement Learning
SN Computer Science (2021) -
Qing Chuan Ye,Jason Rhuggenaath,Yingqian Zhang,Sicco E. Verwer,Michiel Jurgen Hilgeman
Data driven design for online industrial auctions
Annals of Mathematics and Artificial Intelligence (2021) -
Jason Rhuggenaath,Reza Refaei Afshar,Alp Akcay,Yingqian Zhang,Uzay Kaymak,Fatih Çolak,Muratcan Tanyerli
Maximizing revenue for publishers using header bidding and ad exchange auctions
Operations Research Letters (2021)
Ancillary Activities
No ancillary activities