{"type":"ScholarlyArticle","title":"Online Bin Packing with Predictions","authors":[{"name":"Spyros Angelopoulos","slug":"spyros-angelopoulos","given":"Spyros","family":"Angelopoulos","affiliation":"Centre National de la Recherche Scientifique (CNRS); LIP6 , Sorbonne Université , Paris , France"},{"name":"Shahin Kamali","slug":"shahin-kamali","given":"Shahin","family":"Kamali","affiliation":"University of Manitoba , Winnipeg , Manitoba , Canada"},{"name":"Kimia Shadkami","slug":"kimia-shadkami","given":"Kimia","family":"Shadkami","affiliation":"University of Manitoba , Winnipeg , Manitoba , Canada"}],"abstract":"Bin packing is a classic optimization problem with a wide range of applications from load balancing to supply chain management. In this work, we study the online variant of the problem, in which a sequence of items of various sizes must be placed into a minimum number of bins of uniform capacity. The online algorithm is enhanced with a (potentially erroneous) prediction concerning the frequency of item sizes in the sequence. We design and analyze online algorithms with efficient tradeoffs between the consistency (i.e., the competitive ratio assuming no prediction error) and the robustness (i.e., the competitive ratio under adversarial error), and whose performance degrades near-optimally as a function of the prediction error. This is the first theoretical and experimental study of online bin packing in the realistic setting of learnable predictions. Previous work addressed only extreme cases with respect to the prediction error, and relied on overly powerful and error-free oracles.","doi":"10.24963/ijcai.2022/635","url":"https://ijcai.org/proceedings/2022/635","pdf":"https://ijcai.org/proceedings/2022/0635.pdf","markdown":"https://ijcai.org/proceedings/2022/635.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/635","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Planning and Scheduling","track":"Main Track","number":635,"pages":{"first":4574,"last":4580},"keywords":["Planning and Scheduling: Scheduling","Planning and Scheduling: Learning in Planning and Scheduling","Machine Learning: Optimisation","Planning and Scheduling: Planning under Uncertainty"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987065","length_sec":90,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985208","length_sec":719,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Anand et al., 2020] K. 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