Abstract
Assigning tasks to service providers is a frequent procedure across various applications. Often the tasks arrive dynamically while the service providers remain static. Preventing task rejection caused by service provider overload is of utmost significance. To ensure a positive experience in relevant applications for both service providers and tasks, fairness must be considered. To address the issue, we model the problem as an online matching within a bipartite graph and tackle two minimax problems: one focuses on minimizing the highest waiting time of a task, while the other aims to minimize the highest workload of a service provider. We show that the second problem can be expressed as a linear program and thus solved efficiently while maintaining a reasonable approximation to the objective of the first problem. We developed novel methods that utilize the two minimax problems. We conducted extensive simulation experiments using real data and demonstrated that our novel heuristics, based on the linear program, performed remarkably well.
Original language | English |
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Title of host publication | Proceedings of the 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024 |
Editors | Kate Larson |
Publisher | International Joint Conferences on Artificial Intelligence |
Pages | 257-264 |
Number of pages | 8 |
ISBN (Electronic) | 9781956792041 |
State | Published - 2024 |
Event | 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024 - Jeju, Korea, Republic of Duration: 3 Aug 2024 → 9 Aug 2024 |
Publication series
Name | IJCAI International Joint Conference on Artificial Intelligence |
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ISSN (Print) | 1045-0823 |
Conference
Conference | 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024 |
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Country/Territory | Korea, Republic of |
City | Jeju |
Period | 3/08/24 → 9/08/24 |
Bibliographical note
Publisher Copyright:© 2024 International Joint Conferences on Artificial Intelligence. All rights reserved.