Projecten per jaar
Persoonlijk profiel
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.
Vingerafdruk Duik in de onderzoeksthema's waar Jason Rhuggenaath actief is. Deze onderwerplabels komen voort uit het werk van deze persoon. Samen vormen ze een unieke vingerafdruk.
Learning systems
Engineering en materiaalwetenschappen
Planning
Engineering en materiaalwetenschappen
Light trailers
Engineering en materiaalwetenschappen
Integer programming
Engineering en materiaalwetenschappen
Auctions
Rekenkunde
Decision trees
Engineering en materiaalwetenschappen
Experiments
Engineering en materiaalwetenschappen
Display devices
Engineering en materiaalwetenschappen
Netwerk
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Projecten 2017 2018
- 1 Geëindigd
Onderzoeksoutput 2018 2019
A heuristic policy for dynamic pricing and demand learning with limited price changes and censored demand
Rhuggenaath, J., de Oliveira da Costa, P. R., Akcay, A., Zhang, Y. & Kaymak, U., okt 2019, 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) . Piscataway: Institute of Electrical and Electronics Engineers, blz. 3693-3698 6 blz. 8914590Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review
Open Access
Bestand
Dynamic demand
Price changes
Dynamic pricing
Seller
Heuristics
3
Citaties
(Scopus)
92
Downloads
(Pure)
A PSO-based algorithm for reserve price optimization in online ad auctions
Rhuggenaath, J., Akcay, A., Zhang, Y. & Kaymak, U., jun 2019, 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings. Piscataway: Institute of Electrical and Electronics Engineers, blz. 2611-2619 9 blz. 8789915Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review
Open Access
Bestand
Particle swarm optimization (PSO)
Marketing
Experiments
Data driven design for online industrial auctions
Ye, Q. C., Rhuggenaath, J., Zhang, Y., Verwer, S. E. & Hilgeman, M. J., 2019. 7 blz.Onderzoeksoutput: Bijdrage aan congres › Paper
Open Access
Bestand
Display devices
Data-driven policy on feasibility determination for the train shunting problem
De Oliveira Da Costa, P., Rhuggenaath, J., Zhang, Y., Akcay, A., Lee, W-J. & Kaymak, U., 2019. 16 blz.Onderzoeksoutput: Bijdrage aan congres › Paper
Open Access
Bestand
Planning
Scheduling
Parking
Learning systems
Decision making
Data-driven policy on feasibility determination for the train shunting problem (extended abstract)
De Oliveira Da Costa, P., Rhuggenaath, J., Zhang, Y., Akcay, A., Lee, W-J. & Kaymak, U., 2019. 2 blz.Onderzoeksoutput: Bijdrage aan congres › Abstract
Open Access
Bestand
Planning
Scheduling
Parking
Learning systems
Decision making