Learning algorithms to deal with failures in production planning

Abstract

New technologies and the advancement of Industry 4.0 have led to significant changes in production processes, e.g., increased use of data for decision making, faster production speed due to automation, and shorter planning horizons. Soon, the resolution methods and scenarios consolidated in the literature may not adapt to this situation. Therefore, decision-makers must consider this new context in the production planning phase. This work proposes approaches to solve production planning problems in uncertain environments and introduces a framework that predicts the best strategy for implementation according to the specific problem instance. We incorporate characteristics of Industry 4.0 into our study, considering that the delivery of products at their due dates to customers will be more relevant than minimizing costs. The proposed proactive approaches use machine learning algorithms to predict disruptions on the shop floor. We compare strategies considering feedback information on real failures to modify the planning of future periods. Further, we propose a proactive-online approach integrating proactive and real-time decisions, comparing the results with a corrective strategy. Based on computational tests performed with a proposed benchmark, we conclude that the proactive and proactive-online approaches resulted in lower total weighted tardiness in comparison to the corrective method. Regarding the proactive and proactive-online approaches, we observe that their results depend on the set of analyzed instances, justifying the proposition of the framework. Lastly, for most cases, the strategies predicted by the framework achieved lower total weighted tardiness when compared with the average results obtained by all the strategies studied in this work.