A Hierarchical Framework for Optimal and Scalable Process Scheduling in Plant Operations

Abstract

Process scheduling problems are often modeled as mixed-integer nonlinear programs (MINLPs) with a large number of constraints. While meta-heuristics, such as the simulated annealing (SA) algorithm or the genetic algorithm (GA) have been extensively employed to obtain high-quality solutions to MINLPs, their capabilities are limited by the large number of combinatorial constraints and the time required to obtain these solutions. In view of these limitations, this paper presents a hierarchical approach that leverages the capabilities of the satisfiability modulo theory (SMT) for constraint satisfaction and the relative CPU-time competitiveness of the SA algorithm in configuring meta-heuristics for optimal process scheduling subjected to static and temporal constraints. The framework has access to a high-fidelity simulator of the plant, but not the mathematical model. Besides addressing the “hard” operational constraints, our framework also accommodates for “soft” constraints, such as generating schedules that are contiguous and avoid frequent switching between two operational modes.