PhD Defence: Qing Chuan Ye

In his dissertation ‘Multi-objective Optimization Methods for Allocation and Prediction’ Qing Chuan Ye focuses on two different aspects of auctions and employs techniques and methods from both operations research and computer science.
Qing Chuan Ye defended his dissertation in the Senate Hall at Erasmus University Rotterdam on Thursday, 9 May 2019 at 15:30. His supervisors were Prof. Rommert Dekker (RSM) and Dr Yingqian Zhang (TU Eindhoven). Other members of the Doctoral Committee are Prof. Albert Wagelmans (ESE), Dr Niels Agatz (RSM) and Prof.Uzay Kaymak (TU Eindhoven).


About Qing Chuan Ye

Charlie (Qing Chuan) Ye (1989) obtained his BSc Econometrics and Operations Research from Erasmus University Rotterdam in 2009. In 2012 he received his MSc Econometrics and Management Science at the same university, with a specialization in Operations Research and Quantitative Logistics.

Charlie joined the Erasmus Research Institute of Management (ERIM) in October 2012 as a PhD student under the supervision of Prof. Rommert Dekker and Dr Yingqian Zhang.
He worked on multi-objective optimization problems in task allocations and auctions. His work has been published in the journals Artificial Intelligence and Omega. His paper in the journal Omega has received the Best Paper Award for 2017. He has presented his research at various national and international conferences. His research interests include operations research, optimization, algorithm design and machine learning.

During his PhD project he assisted in and taught various courses, primarily numerical methods and programming courses.

Thesis Abstract

In this thesis we focus on two different aspects of auctions and we employ techniques and methods from both operations research and computer science.
First, we study the allocation of tasks to agents at the end of an auction. Usually, tasks are allocated in such a way that minimizes the total cost for the auctioneer. This allocation is optimal in a one-shot auction, but if the auction is repeated, this can have negative consequences for the results in the long run. Therefore, we consider a fair allocation, which costs slightly more in a one-shot auction, but has positive effects on the participation level of agents and the total cost for the auctioneer in repeated auctions.
Second, we consider the auction design. How an auction is set up, like which tasks should be auctioned first, or what the starting price should be, impacts the result. Usually there are experts who know what has occurred in previous auctions and how a future auction should be designed in order to obtain the best results. However, historical auctions can obtain so much information that experts overlook things. We use a combination of machine learning and optimization models to extract information from historical auctions and use this information to help design future auctions for better results.

Photos: Hans / Capital Images