Frye, MaikMaikFryeGyulai, DavidDavidGyulaiBergmann, JuliaJuliaBergmannSchmitt, Robert H.Robert H.Schmitt2022-03-062022-03-062019https://publica.fraunhofer.de/handle/publica/26184610.17973/MMSJ.2019_11_2019051Detailed manufacturing process data and sensor signals are typically disregarded in production scheduling. However, they have strong relations since a longer processing time triggers a change in schedule. Although promising approaches already exist for mapping the influence of manufacturing processes on production scheduling, the variability of the production environment, including changing process conditions, technological parameters and the status of current orders, is usually ignored. For this reason, this paper presents a novel, data-driven approach that adaptively refines the production schedule by applying Machine Learning (ML)-models during the manufacturing process in order to predict the process-dependent parameters that influence the schedule. With the proper prediction of these parameters based on the process conditions, the production schedule is proactively adjusted to changing conditions not only to ensure the sufficient product quality but also to reduce the negative effects and losses that delayed rescheduling would cause. The proposed approach aims on minimizing the overall lateness by utilizing an active data exchange between the scheduling system and the predictive ML-models on the process level. The efficiency of the solution is demonstrated by a realistic case study using discrete event simulation.enArtificial intelligenceMachine learningData analyticsAdaptive schedulingProcess parameter predictionProcess optimizationJob shop scheduling658670journal article