AI Infrastructure Optimization With Weighted Priority Parameters
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Solution Overview
Problem
Conventional travel and transportation management systems rely heavily on human expertise, leading to subjective decisions, inefficiencies, and suboptimal plans due to the lack of data-driven approaches, which fail to consider all relevant factors and adapt to changing conditions.
Innovation Solution
A system and method utilizing machine learning models to optimize infrastructure functions by receiving priority parameters, generating optimized plans, and updating existing systems to align with these parameters, while considering historical data and potential conflicts with existing rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If human expertise and manual processes are used for stand planning and optimization, then operational flexibility and adaptability to changing conditions are maintained, but decision-making efficiency and optimization precision deteriorate due to subjective biases and inability to consider all factors simultaneously
Solution Approach 1:
The patent introduces an AI-based intermediary system that acts as a mediator between human operators and the complex optimization problem. The AI system processes multiple priority parameters, historical data, and operational constraints to generate optimized stand plans, while human operators maintain oversight and can adjust priorities. This intermediary approach eliminates subjective biases while preserving human adaptability to changing conditions.
2Ease of operation
If manual stand allocation based on expert knowledge is used, then operational simplicity and ease of implementation are maintained, but productivity and resource utilization deteriorate due to suboptimal plans that miss revenue opportunities and increase fuel consumption
Solution Approach 1:
The system enables self-service optimization where the AI automatically analyzes operational data, evaluates multiple priority parameters (revenue, fuel consumption, passenger experience), and generates optimized stand plans without requiring deep expert intervention. The system serves itself by continuously learning from historical data and operational outcomes, improving resource utilization while maintaining ease of operation through automated decision-support.
3Reliability
If rule-based systems are used for stand optimization, then consistency and reliability of decisions are improved, but adaptability to new scenarios and complexity of system configuration worsen due to inability to handle indirect cost factors
Solution Approach 1:
The patent implements a dynamic optimization system where priority parameters and their weightings can be adjusted based on changing operational conditions, airline preferences, and external factors. The AI model dynamically re-evaluates stand allocations as new information becomes available, transitioning from static rule-based decisions to adaptive, real-time optimization that maintains reliability while embracing change.
4Measurement precision
If comprehensive data-driven optimization systems are implemented, then optimization precision and productivity are improved, but device complexity and computational requirements worsen due to need to process multiple priority parameters and historical data
Solution Approach 1:
The patent segments the complex optimization problem into manageable components by defining discrete priority parameters (revenue generation, fuel consumption, passenger experience, operational constraints) that can be independently weighted and adjusted. The AI system processes these segmented parameters separately before integrating them into comprehensive stand allocation decisions, reducing computational complexity while maintaining optimization precision.
Data Source
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AI summary
According to a number of embodiments, the present disclosure relates to systems and methods for optimizing infrastructure. A method is provided comprising steps of: a. receiving one or more priority parameters for consideration for optimization, wherein each of the priority parameters relates to a target aspect of the infrastructure associated with one or more functions of the infrastructure and comprises a weighting; b. receiving an existing, first optimization plan from an existing, first optimization system, wherein the first optimization comprises data relating to the target aspect(s); c. generating a recommended, second optimization plan by a second optimization system, wherein the second optimization plan is generated by modifying the first optimization plan in order to optimize the target aspect(s) according to the weighting(s), using a prediction module comprising one or more models; d. transmitting the second optimization plan from the second optimization system to the first optimization system; and e. updating the first optimization plan based on the second optimization plan. A method for prioritizing the one or more priority parameters, a prediction module, and a corresponding system are also provided. A corresponding method and a system for optimizing stand planning are also provided.