Industrial Process Scheduling via Simulation-Based State Transitions

The patent replaces traditional mathematical optimization models (continuous/discrete variables, linear programming) with a simulation-based approach using discrete event simulation and reinforcement learning. This substitution transforms the complex analytical modeling problem into a computational simulation problem, reducing the theoretical complexity while maintaining scheduling precision through iterative learning and evaluation.

The patent changes the fundamental parameters of the scheduling approach by transitioning from fixed mathematical formulations to dynamic simulation models that can adapt to different process conditions. The simulation model incorporates state transitions, event sequences, and learning-based decision-making, allowing the system to handle complexity through parameter adaptation rather than rigid mathematical constraints.

The patent creates a universal simulation framework that can model multiple types of industrial processes (chemical, pharmaceutical, food processing) using common constructs like resources, operations, state transitions, and events. This multi-functional model structure allows the same framework to scale across different domains while maintaining scheduling accuracy, as the simulation approach adapts to various process types without requiring fundamentally different mathematical formulations.

The patent introduces dynamics into the scheduling model through simulation-based state transitions and time-varying parameters. Instead of static optimization models, the system uses dynamic simulation that can capture changing process conditions, resource availability, and operational constraints over time. This dynamic approach improves scalability by allowing the model to adapt to different process scales and complexities while maintaining consistent accuracy through the simulation engine.

The patent introduces an intermediary simulation environment that bridges the gap between complex reinforcement learning algorithms and user-friendly operation. The simulation model acts as a mediator, translating real-world process complexity into a controlled virtual environment where reinforcement learning can operate effectively. This intermediary layer handles the modeling complexity internally while presenting simplified interfaces and configurations to operators, thus improving ease of operation without sacrificing adaptability.

The patent implements self-service capabilities through the reinforcement learning agent that automatically learns optimal scheduling policies from simulation data. The system performs self-training and self-optimization within the simulation environment, reducing the need for manual configuration and expert intervention. This self-service approach improves ease of operation by allowing the system to adapt automatically while the simulation framework manages the underlying complexity autonomously.