Integrated Flight Schedule Optimization with Crew Pairing Constraints
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Solution Overview
Problem
The Airline Scheduling and Routing (ASR) problem is complex due to the large number of possible flight schedules and operational restrictions, making it difficult for airlines to generate optimal schedules that efficiently utilize existing aircraft resources and minimize costs while ensuring crew feasibility.
Innovation Solution
The solution involves a modified column generation approach that simultaneously solves the flight schedule, aircraft routing, and crew pairing problems, incorporating constraints to ensure crew feasibility and flexibility in selecting flights, allowing for the generation of optimized flight schedules that consider permissible crew pairings and optional flights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the ASR problem is solved by dividing it into three separate sub-problems (flight schedule, aircraft routing, and crew pairing), then the complexity of each individual problem is reduced and can be solved more easily, but it becomes impossible to achieve a global optimal solution
Solution Approach 1:
The patent merges the three separate sub-problems (flight schedule generation, aircraft routing, and crew pairing) into a single integrated optimization model. This allows all constraints and objectives to be considered simultaneously, achieving a global optimal solution rather than suboptimal local solutions from sequential solving.
Solution Approach 2:
The patent segments the integrated optimization problem into three modular components that can be solved sequentially using column generation: (1) generate permissible flight schedules, (2) determine optimal aircraft routing, and (3) create valid crew pairings. This segmentation maintains the ability to achieve global optimality while managing computational complexity through structured decomposition.
2Productivity
If the airline generates optimized flight schedules without considering crew pairings in advance, then the flight schedule optimization can be performed more independently and efficiently, but the resulting schedule may not be crew-feasible and requires additional steps to ensure crew pairing validity
Solution Approach 1:
The patent performs preliminary action by generating permissible flight schedules that inherently satisfy crew pairing constraints before finalizing the aircraft routing. By embedding crew pairing feasibility checks into the flight schedule generation phase, the system ensures that subsequent routing decisions are based on already crew-feasible schedules, eliminating the need for separate feasibility corrections.
Solution Approach 2:
The patent implements feedback mechanisms where crew pairing constraints and feasibility information are fed back into the flight schedule and aircraft routing optimization processes. This iterative feedback ensures that scheduling decisions consistently account for crew availability and pairing requirements, maintaining reliability throughout the optimization chain.
3Loss of energy
If the airline uses a fixed number of aircraft to operate flights, then the fixed cost for each aircraft can be controlled, but the ability to optimize schedules becomes limited by the available aircraft resources
Solution Approach 1:
The patent introduces dynamics by allowing the aircraft fleet composition and routing assignments to be dynamically optimized rather than fixed. The system can adaptively allocate different aircraft types to different routes and schedules based on operational requirements, enabling flexible optimization while maintaining cost control through the fixed fleet constraint.
Solution Approach 2:
The patent utilizes parameter changes by optimizing various scheduling parameters (departure times, routing sequences, aircraft assignments) within the constraint of a fixed aircraft fleet. By changing these operational parameters rather than the fundamental fleet size, the system achieves optimization flexibility without violating fixed cost structures.
4Reliability
If the airline includes all possible optional flights in the schedule to ensure market presence and maintain gates/slots, then the airline's market coverage is maximized, but the overall cost and operational complexity increase
Solution Approach 1:
The patent applies local quality by selectively including optional flights based on their specific strategic value to different markets or routes. Rather than uniformly including all optional flights, the system evaluates each flight's contribution to market presence and operational efficiency, including only those that provide optimal local value while maintaining overall schedule simplicity.
Data Source
AI summary
The subject matter of this specification can be embodied in, among other things, a method that includes generating a first graph including possible flight segments between airline stations for an airline and determining a set of permissible crew pairings based on a traversal of the first graph. The method also includes generating a second graph comprising the determined set of permissible crew pairings and determining a set of permissible aircraft routings based on a traversal of the second graph. The method includes generating a set of optimized aircraft routings using an integer-programming algorithm that accepts the determined set of permissible aircraft routings as input, and outputting the set of optimized aircraft routings for use in a flight schedule.