TY - GEN
T1 - Design a Win-Win Strategy That Is Fair to Both Service Providers and Tasks When Rejection Is Not an Option
AU - Trabelsi, Yohai
AU - Xu, Pan
AU - Kraus, Sarit
N1 - Publisher Copyright:
© 2024 International Joint Conferences on Artificial Intelligence. All rights reserved.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85204281503&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85204281503
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 257
EP - 264
BT - Proceedings of the 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024
A2 - Larson, Kate
PB - International Joint Conferences on Artificial Intelligence
T2 - 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024
Y2 - 3 August 2024 through 9 August 2024
ER -