aGS applies evolutionary search to the hardest scheduling problems—minimizing makespan, cutting changeovers, and lifting asset utilization across plants, fleets, and service networks.

By iterating through selection, crossover, and mutation, it converges on near-optimal schedules with minimal human input and adapts quickly as conditions change.

Optimizes production lines, logistics fleets, maintenance crews, and project resources within a unified model—each domain represented as a chromosome population.

Evaluates thousands of candidate schedules per generation, scoring each by KPIs such as throughput, tardiness, resource utilization, and changeover cost.

Captures precedence, tooling, labor, and material constraints; enforces hard limits while flexibly penalizing soft violations for faster convergence.

Intelligently adjusts mutation and crossover probabilities based on population diversity, accelerating convergence without premature lock-in.

Distributes genetic evaluation across multi-core CPUs or GPU clusters, shrinking computation time for large, multi-factory scheduling problems.

Instantly re-optimizes schedules when disruptions occur—machine failure, new order, or rush job—while preserving previous commitments where possible.

REST endpoints and plug-ins synchronize job orders, BOMs, and inventory with existing enterprise systems for closed-loop optimization.