Iterative Optimization Routine for Physical Object Location
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Solution Overview
Problem
Network operators face challenges in determining optimal physical locations for new objects to maximize traffic flow, as current methods rely heavily on experience and are inefficient, leading to varying degrees of success and resource-intensive manual analysis.
Innovation Solution
A computer system executes an iterative optimization routine that evaluates candidate locations using trained demand and overhead models to predict traffic scores, overhead values, and cannibalization factors, generating an overall score to identify optimal locations for deploying new objects, which can be visualized on a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual analysis and experience-based methods are used to determine optimal locations, then deployment decisions can be made, but the process is resource-intensive and inefficient with varying success rates
Solution Approach 1:
The patent replaces manual analysis and experience-based decision making with an automated computerized system that uses machine learning models and optimization algorithms to evaluate candidate locations and determine optimal deployment sites, thereby eliminating resource-intensive manual processes
Solution Approach 2:
The system performs self-evaluation by automatically analyzing traffic data, competition information, and other relevant factors through trained demand models and overhead models to generate location recommendations without requiring external expert intervention
2Area of stationary object
If multiple objects are deployed in a geographical region, then network coverage is improved, but cannibalization occurs where new objects take traffic away from existing objects
Solution Approach 1:
The patent incorporates a cannibalization model that provides feedback on how new objects will impact existing objects in the network. The system evaluates the potential traffic loss to existing objects and uses this information to adjust deployment recommendations, ensuring that new objects are placed where they will not significantly harm existing operations
Solution Approach 2:
The system changes the evaluation parameters by incorporating cannibalization factors into the overall scoring mechanism. This allows the optimization process to account for the negative impacts on existing objects while still pursuing network expansion goals
3Measurement precision
If comprehensive evaluation of candidate locations is performed using multiple models, then location accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the complex evaluation process into distinct modular components: demand models that predict traffic demand, overhead models that estimate operational costs, and cannibalization models that assess impact on existing objects. Each module handles a specific aspect of the evaluation, making the overall system more manageable and interpretable
Data Source
AI summary
In some examples, a system can execute an iterative optimization routine on a set of candidate physical locations for an object. Each iteration can involve a series of operations. The operations can include selecting one of the candidate physical locations; determining a predicted demand value for the candidate physical location based on a first traffic score and a second traffic score associated with the candidate physical location; and determining a predicted overhead value for the candidate physical location. The operations can also include determining a predicted cannibalization factor for the candidate physical location; and generating an overall score for the candidate physical location based on the predicted demand value, the plurality of predicted overhead values, and the predicted cannibalization factor. The system may then identify one or more of the candidate physical locations as optimal locations based on their overall scores and display the optimal locations on a geographical map.