An inventory data-driven model for predictive-reactive production scheduling

Satie L. Takeda-Berger and Enzo M. Frazzon

International Journal of Production Research, 2024, vol. 62, issue 9, 3059-3083

Abstract: Scheduling is a complex task due to the need to optimise multiple competing objectives and react to unpredictable events that may occur during production execution. The strategy of predictive-reactive scheduling can be used to reconcile the conflict between the original schedule and the current shop floor situation. This study seeks to present an inventory data-driven predictive-reactive production scheduling model that supports the evolving concepts of the Industry 4.0. Periodically, a machine learning technique provides predictive scheduling considering a best-case scenario according to an established Key Performance Indicator (KPI). Then, material non-availability causes disruptions in production, which triggers the Simulation-Based Optimization (SBO) method to handle these events. Thus, SBO provides a reactive schedule with the best set of priority rules to sequence jobs on each machine according to the data on the shop floor. This model was validated with a real case study using data collected from a metal-mechanical company. Considering the service level KPI, the results showed that the model is able to find a better solution in the compared scenarios. Therefore, even in a dynamic and stochastic scenario, with machine breakdowns, quality problems, raw material delays, and accuracy issues, the model proved efficient in mitigating these variations’ effects.

Date: 2024
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DOI: 10.1080/00207543.2023.2217297

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