{"type":"ScholarlyArticle","title":"Adaptive Artificial Intelligence Scheduling Methods for Large-Scale, Stochastic, Industrial Applications","authors":[{"name":"Pierre Tassel","slug":"pierre-tassel","given":"Pierre","family":"Tassel","affiliation":"University of Klagenfurt , Austria"}],"abstract":"Traditional scheduling techniques suffer from a lack of flexibility. \r\nThe problem's instances need to be deterministic, and results on datasets with small benchmark instances do usually not transfer to large-scale instances. \r\nWe propose to develop adaptive algorithms that can leverage the similarities between instances of industrial scheduling problems. \r\nIn particular, we focus on applications of modern machine learning techniques to combinatorial optimization problems, an emerging and promising research area. \r\nTraditional scheduling techniques such as constraint, mixed-integer, or answer set programming are highly generic, domain-independent, and, therefore, do not explicitly exploit the specificities of a problem domain. \r\nHowever, in a production facility, the settings between two consecutive schedules are often very similar.\r\nThe machines, workers, production capacity, etc., usually stay the same or do not change significantly.\r\nTraditional scheduling techniques do not take advantage of such similarities, while machine learning, especially deep learning, can discover and exploit relationships in the data. \r\nTherefore, our research aims to incorporate machine learning into combinatorial optimization.","doi":"10.24963/ijcai.2022/841","url":"https://ijcai.org/proceedings/2022/841","pdf":"https://ijcai.org/proceedings/2022/0841.pdf","markdown":"https://ijcai.org/proceedings/2022/841.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/841","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":"Doctoral Consortium","track":"Doctoral Consortium","number":841,"pages":{"first":5877,"last":5878},"keywords":["Machine Learning (ML): General","Planning, Routing, and Scheduling (PRS): General","Constraint Satisfaction and Optimization (CSO): General"],"videos":[],"references":[{"position":1,"raw":"[Bengio et al., 2021] Yoshua Bengio, Andrea Lodi, and An- toine Prouvost. 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A multi-shot ASP encoding for the aircraft routing and maintenance planning problem","authors":"Rbaia Tassel","year":"2021","venue":"JELIA","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Tassel et al., 2021] Pierre Tassel, Martin Gebser, and Kon- stantin Schekotihin. A reinforcement learning environ- ment for job-shop scheduling. CoRR, abs/2104.03760, 2021.","title":"A reinforcement learning environment for job-shop scheduling","authors":"Tassel","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Tassel et al., 2022] Pierre Tassel, Benjamin Kovács, Martin Gebser, Konstantin Schekotihin, Wolfgang Kohlenbrein, and Philipp Schrott-Kostwein. Reinforcement Learning of Dispatching Strategies for Large-Scale Industrial Schedul- ing. ICAPS, 2022.","title":"Reinforcement Learning of Dispatching Strategies for Large-Scale Industrial Scheduling","authors":"Tassel","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}