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

Syeda Marzia, Ahmed Azab () and Alejandro Vital-Soto
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Syeda Marzia: Production & Operations Management Research Lab, Industrial and Manufacturing System Engineering Department, University of Windsor, Windsor, ON N9B 3P4, Canada
Ahmed Azab: Production & Operations Management Research Lab, Industrial and Manufacturing System Engineering Department, University of Windsor, Windsor, ON N9B 3P4, Canada
Alejandro Vital-Soto: Shannon School of Business, Cape Breton University, Sydney, NS B1M 1A2, Canada

Mathematics, 2025, vol. 13, issue 16, 1-31

Abstract: Manufacturing industries are undergoing a significant transformation toward Smart Manufacturing (SM) to meet the ever-evolving demands for 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, this 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 connects CAPP and scheduling by considering setups as dispatching units, ultimately reducing makespan and improving manufacturing flexibility. The problem is modeled as a Dynamic Flexible Job Shop problem. It is tackled through a comprehensive methodology that combines mathematical programming, heuristic techniques, and the creation of a robust dataset capturing priority relationships among setups. Empirical results demonstrate that the proposed model achieves a 42.6% reduction in makespan, improves schedule robustness by 35%, and reduces schedule variability by 27% compared to classical dispatching rules. Additionally, the model achieves an average prediction accuracy of 92% on unseen instances, generating rescheduling decisions within seconds, which confirms its suitability for real-time Smart Manufacturing applications.

Keywords: process planning; dynamic scheduling; supervised classification learning; smart manufacturing; optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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