A Framework for Iterative Verification and Validation of Prescriptive MILP Scheduling Models Based on Petri Nets

This work presents an iterative framework to improve the verification and validation of Mixed Integer Linear Programming (MILP) scheduling models using Petri Nets (PN). While MILP models are widely utilized for optimizing resource allocation and scheduling tasks due to their mathematical rigor and ability to guarantee optimal solutions, challenges persist in verifying their logical consistency and validating their applicability in real-world scenarios. These challenges arise from the complexity of large-scale models, the subjectivity in their construction, and the limited ability to compare theoretical outcomes with real-world data. To address these issues, the proposed framework leverages Petri Nets as a complementary tool to MILP. By combining MILP and PN methods, this approach offers a robust mechanism for ensuring the accuracy, feasibility, and practical utility of scheduling models, thereby contributing to the advancement of both theoretical and applied scheduling methodologies.