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.

Additional Metadata
Keywords Auctions, task allocation, fairness, repeated auctions, auction design, machine learning, mathematical model, algorithm
Promotor R. Dekker (Rommert) , Y. Zhang (Yingqian)
Publisher Erasmus University Rotterdam
ISBN 978-90-5892-539-8
Persistent URL hdl.handle.net/1765/116462
Series SIKS Dissertation Series, ERIM Ph.D. Series Research in Management
Note For copyright reasons there is a partial embargo for this dissertation
Citation
Ye, Q.C. (2019, May 9). Multi-objective Optimization Methods for Allocation and Prediction (No. EPS-2019-460-LIS). ERIM Ph.D. Series Research in Management. Erasmus University Rotterdam. Retrieved from http://hdl.handle.net/1765/116462