Why aircraft scheduling becomes difficult
A flight program may look straightforward until individual aircraft must be assigned. Each tail has its own availability, fleet characteristics, location, and maintenance position. Every assignment also affects what becomes possible later in the rotation.
A choice that looks good for one flight can create an impossible connection, leave an aircraft at the wrong station, or make maintenance harder to complete. That is why the whole network must be considered, not just one flight at a time.
A shared operational foundation
Both optimization engines evaluate schedules against the same operational requirements. The search strategy changes, but the rules that define a workable schedule remain consistent.
- Minimum turnaround times allow realistic ground connections.
- Station continuity keeps each aircraft in the right location.
- Fleet and aircraft compatibility limit assignments to valid combinations.
- Availability windows and service periods define when each aircraft can operate.
- Grouped activities can remain together when they form one operational unit.
The Genetic Algorithm and Pathy search for solutions differently while applying the same operational constraints and cost framework to assess schedule quality.
Choose your optimization approach
Different operational scenarios can benefit from different optimization strategies. OptCo provides two approaches while keeping the operational inputs and result analysis consistent.
Genetic Algorithm
The Genetic Algorithm uses an evolutionary search to explore many complete scheduling alternatives. Over successive generations, it combines and improves solutions to discover high-quality aircraft assignments across a large and complex search space.
Its strength is broad exploration and the ability to discover solutions through continuous evolutionary improvement.
Pathy
Pathy is a path-based optimization algorithm developed specifically for complex aircraft assignment and recovery scenarios.
Pathy uses column generation to build relevant operational possibilities and evaluates how those possibilities can work together across the complete schedule. Rather than considering each aircraft independently, it solves a global coordination problem: selecting a compatible combination of aircraft paths that covers flights, respects operational constraints, and minimizes the overall cost of the solution.
This allows Pathy to consider the effect of each assignment on the complete operation—not only on an individual aircraft or flight.
Three principles behind Pathy
1. Build feasible operational paths
Pathy identifies realistic ways aircraft can operate flights while respecting scheduling, positioning, compatibility, and maintenance requirements.
2. Expand the available choices
Through column generation, Pathy introduces additional alternatives where they may improve the overall schedule.
3. Coordinate the complete solution
Pathy selects a globally compatible combination of paths, balancing flight coverage with operational cost and schedule quality.
Genetic Algorithm vs. Pathy
Both engines solve the same operational problem through different optimization strategies.
Genetic Algorithm
- Evolutionary optimization
- Explores many complete schedules
- Focuses on broad solution-space exploration
- Improves solutions over successive generations
Pathy
- Path-based optimization
- Coordinates feasible aircraft paths
- Focuses on structured global coordination
- Expands and combines relevant operational alternatives
Both approaches use the same operational constraints and cost framework, so their results can be reviewed through the same reporting and visualization experience.
A point-in-time benchmark compares identical one-, two-, and three-day scheduling scenarios with 82 aircraft. Each result represents one run rather than an average.
Represent airline policy, not only technical compatibility
Two assignments can both be technically possible while one is clearly better for the operation. OptCo’s rules matrix lets planners describe matches as preferred, non-preferred, or forbidden. Those distinctions make airline-specific policies part of the optimization model.
This adds detail that a simple compatibility table cannot show. It also lets planners change how the search treats specific activity-resource combinations.
Connect flying with maintenance routing
Maintenance requirements affect where an aircraft can go. OptCo checks whether a station can perform the work and has enough capacity, so maintenance becomes part of the rotation.
Review results consistently
Whichever optimization engine is used, planners review the resulting schedule through the same visualization experience. Flight coverage, aircraft assignments, operational penalties, and total cost remain visible in a consistent format.
That shared view makes it easier to understand tradeoffs and compare results from the same scheduling problem without changing how the output is reviewed.