{"type":"ScholarlyArticle","title":"Design a Win-Win Strategy That Is Fair to Both Service Providers and Tasks When Rejection Is Not an Option","authors":[{"name":"Yohai Trabelsi","slug":"yohai-trabelsi","given":"Yohai","family":"Trabelsi","affiliation":"Department of Computer Science , Bar-Ilan University , Ramat Gan , Israel"},{"name":"Pan Xu","slug":"pan-xu","given":"Pan","family":"Xu","affiliation":"New Jersey Institute of Technology , Newark , NJ , USA"},{"name":"Sarit Kraus","slug":"sarit-kraus","given":"Sarit","family":"Kraus","affiliation":"Department of Computer Science , Bar-Ilan University , Ramat Gan , Israel"}],"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.\r\nTo ensure a positive experience in relevant applications for both service providers and tasks, fairness must be considered. \r\nTo 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.","doi":"10.24963/ijcai.2024/29","url":"https://ijcai.org/proceedings/2024/29","pdf":"https://ijcai.org/proceedings/2024/0029.pdf","markdown":"https://ijcai.org/proceedings/2024/29.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/29","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Agent-based and Multi-agent Systems","track":"Main Track","number":29,"pages":{"first":257,"last":264},"keywords":["Agent-based and Multi-agent Systems: MAS: Resource allocation"],"videos":[],"references":[{"position":1,"raw":"[Ackerman Viden et al., 2023] Osnat Ackerman Viden, Yohai Trabelsi, Pan Xu, Karthik Abinav Sankararaman, Oleg Maksimov, and Sarit Kraus. 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