One of the easiest mistakes in a manufacturing simulator is inventing formulas that look realistic.
It is tempting to say a cutting job should take a fixed number of seconds per inch, add a multiplier for layer count, add another multiplier for fabric type, and call that a timing model. The result may feel more sophisticated than a placeholder, but it can be worse than admitting the model is not ready yet.
Fabricated realism creates misleading behavior. If the simulator rewards decisions based on made-up timing rules, any future agent, benchmark, or scheduling strategy will optimize against those assumptions instead of the real cutting process. The numbers may be precise, but precision is not the same as credibility.
Cutting Table RL is intentionally avoiding that trap. The simulator currently uses placeholder timing values such as processing_ticks_per_job to move jobs through the machine lifecycle. That value exists to test state transitions, workday execution, metrics, and lifecycle behavior. It is not presented as an accurate cutting-time estimate.
The next timing models should come from real manufacturing data: actual job lengths, layer counts, fabric categories, operator workflow, machine behavior, setup patterns, stops, delays, and completion records. Those observations should shape the simulator instead of being forced to fit a guessed equation.
Simulation credibility matters before optimization. A weak environment can make an optimization method look successful while teaching it behavior that would fail in production. A believable environment gives later experiments a better chance of measuring something useful.
The lesson is simple: build believable environments before optimizing decisions inside them.