{"type":"ScholarlyArticle","title":"Scalable ML Methods to Optimize KPIs in Real-World Manufacturing Processes","authors":[{"name":"Benjamin Kovács","slug":"benjamin-kovacs","given":"Benjamin","family":"Kovács","affiliation":"University of Klagenfurt"}],"abstract":"The goal of this work is to develop novel methods to solve the semiconductor fab scheduling problem. The problem can be modeled as a flexible job-shop with large instances and specific constraints related to special machine and job characteristics. To investigate the problem, we develop a tool to simulate small to large-scale instances of the problem. Using the simulator, we aim to develop new dispatching strategies using genetic programming and reinforcement learning.","doi":"10.24963/ijcai.2022/831","url":"https://ijcai.org/proceedings/2022/831","pdf":"https://ijcai.org/proceedings/2022/0831.pdf","markdown":"https://ijcai.org/proceedings/2022/831.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/831","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":831,"pages":{"first":5857,"last":5858},"keywords":["Machine Learning (ML): General","Planning, Routing, and Scheduling (PRS): General"],"videos":[],"references":[{"position":1,"raw":"[Brockman et al., 2016] Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. 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