Integrated Process Planning and Scheduling Using an Optimized Rule-Mining Approach for Smart Manufacturing

Manufacturing industries are undergoing a significant transformation towards Smart Manufacturing (SM) to cater to the ever-evolving demands of customized products. A major obstacle in this transition is the integration of Computer-Aided Process Planning (CAPP) with Scheduling. This integration poses challenges because of conflicting objectives that must be balanced, resulting in the Integrated Process Planning and Scheduling problem. In response to these challenges, our research introduces a novel hybridized machine learning-optimization approach designed to assign and sequence setups in Dynamic Flexible Job Shop environments via dispatching rule mining, accounting for real-time disruptions such as machine breakdowns. This approach seeks to bridge the gap between CAPP and scheduling by treating setups as dispatching units, ultimately minimizing makespan and bolstering manufacturing flexibility. The problem is modeled as a Dynamic Flexible Job Shop problem, and it is tackled through a comprehensive methodology that combines mathematical programming, heuristic techniques, and the creation of a robust dataset for data mining, which captures attributes reflecting priority relationships among setups. Empirical results validate the effectiveness of our methodology, demonstrating that the mining model surpasses classical dispatching rules. Furthermore, our model exhibits robust generalization capabilities in the context of SM, paving the way for more efficient and adaptive production.