That future is here.
Welcome to EvoAPS – a next-generation AI scheduling engine that learns, adapts, and improves your production plan in real time.
What if your schedule could improve itself?
Every planner knows the feeling: the production plan is built, the lines look ready — and then reality happens. A changeover takes too long. An oven becomes a bottleneck. A supplier pushes back on delivery. Suddenly the “final” schedule is not so final.
For years, planners have relied on heuristic rules, spreadsheets, and experience-led adjustments. Even the best scheduling systems rely heavily on how well a model has been configured by a skilled expert.
But what if your scheduling tool could think differently?
What if it could explore thousands of possibilities every minute?
What if it could continuously improve the schedule — without starting again, without manual rules, and without waiting for a planner to intervene?
To validate performance, we compared EvoAPS with schedules built in Siemens Opcenter APS — including “trained models” configured by expert implementers with decades of scheduling knowledge. The outcome was clear.
Built on Evolutionary Algorithms. Designed for Real-World Manufacturing.
Before launching EvoAPS, we put it through intensive testing using real manufacturing patterns — the kind you see every day on a busy shop floor:
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Single- and multi-operation processes
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Complex calendars and finite resource constraints
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Sequence-dependent changeovers
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Labour and secondary constraints
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Internal & external material dependencies
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BoM-driven task relationships
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Resources that run multiple operations simultaneously (ovens, curing, batching)
Instead of testing each element in isolation, we built models that combined them — because real factories rarely operate in neat, separate boxes. EVO APS was tested against the challenges planners actually face.
From the demo model test (EvoAPS Results Kudos Demo V1):
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EvoAPS delivered an overall schedule up to 11 hours 38 minutes faster than the baseline due-date model.
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After removing off-shift periods, the real-world gain still stood at 3 hours 38 minutes.
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Setup time dropped by nearly 70% — from 4h 58m to just 1h 35m.
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Late orders were eliminated, dropping from 2 late operations to zero.
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Even when compared with advanced, heuristic-driven logic, EvoAPS improved schedule length and reduced setup time by over 60%.
These improvements were not one-off wins. They were consistent across strategies.
In short: EvoAPS outperformed both baseline logic and expert-configured models — while learning independently, without handcrafted rules.
EvoAPS doesn’t just schedule — it evolves.
To validate performance, we compared EvoAPS with schedules built in Siemens Opcenter APS — including “trained models” configured by expert implementers with decades of scheduling knowledge. The outcome was clear.
From the demo model test (EvoAPS Results Kudos Demo V1):
-
EvoAPS delivered an overall schedule up to 11 hours 38 minutes faster than the baseline due-date model.
-
After removing off-shift periods, the real-world gain still stood at 3 hours 38 minutes.
-
Setup time dropped by nearly 70% — from 4h 58m to just 1h 35m.
-
Late orders were eliminated, dropping from 2 late operations to zero.
-
Even when compared with advanced, heuristic-driven logic, EvoAPS improved schedule length and reduced setup time by over 60%.
These improvements were not one-off wins. They were consistent across strategies.
In short: EvoAPS outperformed both baseline logic and expert-configured models — while learning independently, without handcrafted rules.
Seeing the Improvements First-Hand
EvoAPS includes a clear comparison view that lets planners see improvements instantly.
Results are displayed side-by-side against the existing schedule, with improvements shown in green and regressions in red. A read-only Gantt chart provides a visual view of the final schedule, and the Generation History reveals exactly where and when improvements were made throughout the evolutionary process.
This transparency helps planners understand how the schedule evolved — not just which answer it landed on.
What This Means For
Manufacturers
Significantly reduced setup and changeover costs
Shorter makespan and more capacity without changing shifts or adding labour
Better use of equipment and resources
Less firefighting and re-sequencing
More stable, predictable schedules
A schedule that adapts — and improves — instead of staying static
