Hybrid Heuristic Flight Path Optimization System
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Solution Overview
Problem
Current air traffic management systems face challenges in efficiently optimizing flight paths due to unpredictable weather conditions and high controller workload, with existing methods lacking flexibility and realism in simulating traffic flow and congestion management.
Innovation Solution
A hybrid heuristic method combining top-down and bottom-up approaches with genetic algorithms and a realistic air traffic simulator to quickly reoptimize flight routes in response to changing weather conditions, incorporating domain knowledge and problem-specific strategies for selecting optimal flight paths that minimize congestion and flight miles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional air traffic management systems are used to optimize flight paths, then controller workload is high and system flexibility is limited, but implementing advanced optimization systems increases device complexity and requires more computational resources
Solution Approach 1:
The patent introduces an intermediary optimization system that acts as a mediator between flight operators and air traffic controllers. This system automatically processes flight path requests, evaluates multiple route options, and selects optimal paths based on predefined criteria, thereby reducing controller workload while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The optimization system enables flight operators to self-service by automatically generating and selecting optimal flight paths without requiring extensive controller intervention. The system uses automated algorithms to evaluate weather conditions, airspace constraints, and operational preferences to determine the best routes independently
2Reliability
If deterministic optimization methods are used, then solution reliability is high, but the system cannot adapt to dynamic weather conditions and changing operational requirements
Solution Approach 1:
The patent implements a dynamic optimization approach where the system continuously monitors weather conditions, airspace changes, and operational parameters, automatically adjusting flight path recommendations in real-time. The optimization criteria and constraints are dynamically updated based on current conditions rather than being fixed in advance
Solution Approach 2:
The system incorporates feedback mechanisms where optimization results are evaluated against actual weather conditions and operational outcomes. This feedback loop allows the system to learn from past performance and continuously improve its adaptability while maintaining solution reliability through validated optimization algorithms
3Measurement precision
If comprehensive simulation models are used to assess traffic flow impacts, then measurement precision is high, but computation time increases significantly
Solution Approach 1:
The patent segments the comprehensive simulation model into modular components that can be executed independently and in parallel. By dividing the traffic flow assessment into discrete, manageable segments, the system achieves high measurement precision through detailed modeling while reducing overall computation time through parallel processing and selective execution of model components
Data Source
AI summary
Hybrid-heuristic optimization of competing portfolios of flight paths for flights through one or more sectors of an airspace represented by an air traffic system. In one embodiment, a hybrid-heuristic optimization process (100) includes one or more heuristic based processes (110), a genetic optimization process (120), an evaluation process involving an approximation model (130), an optimal portfolio selection process (140) and a validation process involving simulation (150) of the air traffic system.