Enhancing Large-Scale Production Scheduling Using Machine-Learning Techniques

This study focuses on optimizing production scheduling in multi-product plants with shared resources and costly changeover operations. Specifically, two main challenges are addressed, the unknown changeover behavior of new products and the need for rapid schedule generation after unforeseen events. An innovative framework integrating Machine Learning (ML) techniques with Mixed-Integer Linear Programming (MILP) is proposed for single-stage production processes. Initially, a regression model predicts unknown changeover times based on key product attributes. Then, a representation where distances correlate with changeover times is compiled through multidimensional scaling, allowing constrained clustering to group production orders according to available packing lines. Ultimately, the MILP model generates the production schedule within a constrained solution space, utilizing optimal product-to-line allocation from cluster segmentation. A case study inspired by a Greek construction materials plant is used to validate the proposed approach. The results showed that this framework improves scheduling efficiency by providing rapid solutions, reducing downtime and facilitating the introduction of new products. Overall, a novel scheduling solution is proposed for manufacturing industries that face unknown production data and need quick schedule alternations.