Airline Fleet Rescheduling Optimization Engine
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems for rescheduling resources during disruptions, such as in the airline industry, often take too long to find optimal solutions and tend to produce minor variations of the same suboptimal solutions, failing to consider the impact on multiple coordinated resources, leading to inefficient use of critical resources and time.
Innovation Solution
A method using an optimization engine that periodically loads schedule data and applies a local search algorithm in two phases with artificially relaxed and normal costs to generate a plurality of structurally different solutions, integrating fleet, crew, and passenger optimization engines to evaluate and refine solutions, ensuring minimal policy violations and optimality gaps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If brute force algorithms are used to find optimal solutions for resource rescheduling, then solution optimality is improved, but computation time increases unduly
Solution Approach 1:
The patent changes the parameter of cost evaluation by introducing artificially relaxed costs in the first phase that differ from normal costs. This allows the algorithm to explore diverse solutions without being constrained by strict cost optimality, thereby reducing computation time while still producing acceptable solutions. The cost parameters are modified to facilitate faster exploration of the solution space.
Solution Approach 2:
The patent applies partial action by implementing a two-phase approach where the first phase uses relaxed costs to generate diverse solutions without achieving full optimality. This partial optimization in phase one, followed by normal cost evaluation in phase two, avoids the need for exhaustive brute force search while still producing acceptable solutions within reasonable time frames.
2Measurement precision
If optimization engines process solutions for one resource independently, then resource-specific optimization is improved, but overall operation feasibility deteriorates
Solution Approach 1:
The patent merges multiple optimization engines (fleet, crew, passenger) into an integrated system where solutions are exchanged and evaluated across all resources. The fleet optimization engine generates solutions that are then evaluated by crew and passenger engines, ensuring overall operation feasibility. This combining of previously independent engines resolves the contradiction by maintaining resource-specific optimization while ensuring global feasibility.
Solution Approach 2:
The patent implements feedback mechanisms where optimization engines evaluate solutions generated by other engines. The crew optimization engine evaluates fleet solutions, and the passenger optimization engine evaluates crew solutions, providing feedback that ensures overall feasibility. This feedback loop allows each engine to maintain its resource-specific optimization while considering the impact on other resources.
3Measurement precision
If normal costs are used from the beginning in the optimization algorithm, then solution quality is improved, but the ability to generate structurally different solutions decreases
Solution Approach 1:
The patent applies periodic action by implementing a two-phase optimization process. Phase one uses artificially relaxed costs to generate diverse solutions, and phase two uses normal costs to evaluate and refine those solutions. This periodic switching between different cost regimes allows the system to generate structurally different solutions while maintaining solution quality through the second phase evaluation.
Solution Approach 2:
The patent segments the optimization process into two distinct phases with different cost structures. The first phase focuses on generating diverse solutions with relaxed cost constraints, while the second phase evaluates and refines those solutions using normal costs. This segmentation allows the system to achieve both solution diversity and quality by addressing them in separate stages.
Data Source
AI summary
A fleet engine, a crew engine, a passenger engine and an integration engine that communicate with a distributed computer network via two-way communication channels to monitor and repair disruptions to schedules particularly in the airline industry. When a disruption occurs, the method will produce a plurality of solutions that are structurally different for evaluation by the controller or operations manager. The method of generating solutions includes two phases. A first phase with artificially relaxed costs and a second phase with costs that reflect the policies and actual costs of the relevant activities. Upon creating structurally different solutions, the method evaluates the solutions and presents summary information about the solutions to the operations manager. If a violation of a rule occurs in a solution, an alert is generated to notify a user of the rule violation.