{"type":"ScholarlyArticle","title":"A Unified Model for the Two-stage Offline-then-Online Resource Allocation","authors":[{"name":"Yifan Xu","slug":"yifan-xu","given":"Yifan","family":"Xu","affiliation":"Key Lab of CNII , MOE , Southeast University , Nanjing , China; School of Cyber Science and Engineering , Southeast University , Nanjing , China; Department of Computer Science , University of Victoria , Victoria , Canada"},{"name":"Pan Xu","slug":"pan-xu","given":"Pan","family":"Xu","affiliation":"Department of Computer Science , New Jersey Institute of Technology , Newark , USA"},{"name":"Jianping Pan","slug":"jianping-pan","given":"Jianping","family":"Pan","affiliation":"Department of Computer Science , University of Victoria , Victoria , Canada"},{"name":"Jun Tao","slug":"jun-tao","given":"Jun","family":"Tao","affiliation":"Key Lab of CNII , MOE , Southeast University , Nanjing , China; School of Cyber Science and Engineering , Southeast University , Nanjing , China"}],"abstract":"With the popularity of the Internet, traditional offline resource allocation has evolved into a new form, called online resource allocation. It features the online arrivals of agents in the system and the real-time decision-making requirement upon the arrival of each online agent. Both offline and online resource allocation have wide applications in various real-world matching markets ranging from ridesharing to crowdsourcing. There are some emerging applications such as rebalancing in bike sharing and trip-vehicle dispatching in ridesharing, which involve a two-stage resource allocation process. The process consists of an offline phase and another sequential online phase, and both phases compete for the same set of resources. In this paper, we propose a unified model which incorporates both offline and online resource allocation into a single framework. Our model assumes non-uniform and known arrival distributions for online agents in the second online phase, which can be learned from historical data. We propose a parameterized linear programming (LP)-based algorithm, which is shown to be at most a constant factor of 1/4 from the optimal. Experimental results on the real dataset show that our LP-based approaches outperform the LP-agnostic heuristics in terms of robustness and effectiveness.","doi":"10.24963/ijcai.2020/581","url":"https://ijcai.org/proceedings/2020/581","pdf":"https://ijcai.org/proceedings/2020/0581.pdf","markdown":"https://ijcai.org/proceedings/2020/581.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/581","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Planning and Scheduling","track":"Main track","number":581,"pages":{"first":4206,"last":4212},"keywords":["Planning and Scheduling: Scheduling","Agent-based and Multi-agent Systems: Resource Allocation","Agent-based and Multi-agent Systems: Economic Paradigms, Auctions and Market-Based Systems"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25173/file_25173.m3u8","length_sec":299,"provider":"hls"}],"references":[{"position":1,"raw":"[Ashlagi et al., 2019] Itai Ashlagi, Maximilien Burq, Chin- moy Dutta, Patrick Jaillet, Chris Sholley, and Amin Saberi. 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