Abstract
The state task network (STN) representation is a widely used modeling approach for optimal multipurpose batch production scheduling. In practice, STNs have been traditionally formulated as mixed-integer programming (MIP) problems and solved using general-purpose MIP solvers relying on branch-and-bound and branch-and-cut. In the meantime, alternative modeling and solution paradigms for optimization have been developed, enabling the incorporation of alternative variable types and optimization algorithms. Specifically, this work relies on the Hexaly software, which introduced set-based models and their solution through general-purpose hybrid algorithms, i.e., methods that combine traditional MIP with constraint programming, local search, large neighborhood search, among other tools. So far, Hexaly has shown promising results when tackling optimal scheduling problems, however, set-based models and solution approaches for STN optimization have not been studied in the literature. Aiming to fill this gap, this work introduces the first set-based models for the STN problem and performs preliminary benchmarking tests with Hexaly's general-purpose hybrid algorithms. Results from two case studies suggest that set-based models may perform better than the traditional MIP STN formulation when dealing with a simple STN with sequential connectivity and a long-term scheduling instance of a STN with fixed integer batching variables. Overall, this work establishes the foundations for advancing research on set-based approaches for network scheduling, opening new directions beyond traditional MIP optimization.