Aircraft Flight Parameter Management for Multi-Objective Cost Optimization
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Solution Overview
Problem
Current cost index systems in aircraft flight management are limited in optimizing for modern airline objectives, such as environmental impact and noise, and do not facilitate overarching multi-objective optimization across flight networks, fleet allocation, or planning levels.
Innovation Solution
A method using electronic circuitry to manage flight parameters with a 'smart index' that considers multiple cost factors, including financial, environmental, and meteorological costs, optimizing flight parameters like vertical trajectory, lateral trajectory, speed, and thrust, and recalculating these parameters in real-time to adapt to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the cost index is used to optimize flight parameters, then fuel consumption and flight time costs are minimized, but the system cannot incorporate modern airline objectives such as environmental impact, noise, and other operational costs
Solution Approach 1:
The cost function is segmented into multiple independent cost factors (fuel cost, flight time cost, environmental cost, noise cost, etc.), each with its own weighting coefficient. This allows the system to independently adjust and optimize for different objectives without requiring complete redesign of the cost structure.
Solution Approach 2:
The cost function F is designed as a universal multi-objective optimization framework that can simultaneously accommodate traditional operational costs (fuel, time) and modern sustainability objectives (environmental impact, noise). The generic form F = Σ(Ci × Fi) allows any combination of cost factors to be integrated, making the system universally applicable to diverse airline objectives.
2Productivity
If the cost index is used for individual mission optimization, then flight-specific costs are minimized, but overarching optimization at network level, planning level, or fleet allocation level cannot be performed
Solution Approach 1:
The optimization system implements a nested hierarchical structure where individual flight optimization (mission level) is nested within fleet optimization (network level), which is nested within overall airline operations optimization (planning level). Each level uses the same cost function framework but operates at different scopes, allowing comprehensive multi-level optimization without system redesign.
Solution Approach 2:
The cost function framework extends optimization from the traditional single dimension of individual flight missions to multiple dimensions including network level, planning level, and fleet allocation level. This dimensional expansion allows simultaneous optimization across different operational hierarchies while maintaining the same fundamental optimization approach.
3Quantity of substance
If traditional cost index parameters are used, then simple two-factor optimization (fuel and time) is achieved, but comprehensive multi-factor optimization including environmental and operational costs is not possible
Solution Approach 1:
The system transforms the traditional two-parameter cost index into a multi-parameter cost function framework. By introducing weighting coefficients (C1, C2, C3, ...) for different cost factors and allowing dynamic adjustment of these parameters, the system achieves flexible multi-factor optimization while maintaining mathematical tractability and computational efficiency.
Data Source
AI summary
A system for managing flight parameters of aircraft is parametrized with overarching flight cost objectives. For a first flight, parameters of a cost function for the first flight are determined relative, respectively, to different cost factors. Flight parameters are optimized so as to minimize the cost function. Avionics of an aircraft carrying out the first flight are programmed with the flight parameters. On detecting an event in flight requiring the flight parameters to be revised, the parameters of the cost function are recalculated, as well as the flight parameters, and the avionics are reprogrammed accordingly. The method is repeated for at least a second flight, taking into account the effective contribution of the first flight to the overarching objectives, and so on. Thus, an airline can carry out overarching multi-objective optimization on its flights.