{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:38:30Z","timestamp":1723016310192},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:p>Traditional scheduling techniques suffer from a lack of flexibility. \n\nThe problem's instances need to be deterministic, and results on datasets with small benchmark instances do usually not transfer to large-scale instances. \n\nWe propose to develop adaptive algorithms that can leverage the similarities between instances of industrial scheduling problems. \n\nIn particular, we focus on applications of modern machine learning techniques to combinatorial optimization problems, an emerging and promising research area. \n\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. \n\nHowever, in a production facility, the settings between two consecutive schedules are often very similar.\n\nThe machines, workers, production capacity, etc., usually stay the same or do not change significantly.\n\nTraditional scheduling techniques do not take advantage of such similarities, while machine learning, especially deep learning, can discover and exploit relationships in the data. \n\nTherefore, our research aims to incorporate machine learning into combinatorial optimization.<\/jats:p>","DOI":"10.24963\/ijcai.2022\/841","type":"proceedings-article","created":{"date-parts":[[2022,7,16]],"date-time":"2022-07-16T02:55:56Z","timestamp":1657940156000},"page":"5877-5878","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive Artificial Intelligence Scheduling Methods for Large-Scale, Stochastic, Industrial Applications"],"prefix":"10.24963","author":[{"given":"Pierre","family":"Tassel","sequence":"first","affiliation":[{"name":"University of Klagenfurt, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"31","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2022","name":"Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}","start":{"date-parts":[[2022,7,23]]},"theme":"Artificial Intelligence","location":"Vienna, Austria","end":{"date-parts":[[2022,7,29]]}},"container-title":["Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T11:11:52Z","timestamp":1658142712000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2022\/841"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2022\/841","relation":{},"subject":[],"published":{"date-parts":[[2022,7]]}}}