The Trained Model: Capturing and Scaling Knowledge

In traditional scheduling systems, businesses often rely on what the industry refers to as a “trained model.” This means the scheduling logic—whether built using standard APS rules, sequence preferences, or fully custom algorithms—has been configured by someone with deep domain knowledge to achieve a specific, desired result.

Regardless of the method used, the outcome is inherently subjective. It reflects the experience, preferences, and decision-making patterns of a domain expert who guides the system’s behaviour, often in collaboration with the implementor.

But this raises a critical question: how do we capture and scale this knowledge?

Let’s consider a highly experienced planner who has built five schedules per day, five days a week, for 30 years. That equates to roughly 35,350 schedules crafted over a career.

With EvoAPS, we can build and evaluate over 2,000 schedules per minute. In just under 20 minutes, the system can simulate the equivalent of three decades of human scheduling experience—objectively, consistently, and without fatigue.

Conclusion: Scaling Expertise Through Intelligent Automation

EvoAPS enables organizations to move beyond the limitations of individual expertise by capturing strategic intent and applying it at scale. This doesn't just replicate expert knowledge—it amplifies it.

By combining evolutionary algorithms, objective strategy evaluation, and high-speed processing, we can gain deeper insights, faster—helping businesses implement smarter, more adaptive scheduling solutions.