Dynamic Path Selection for Alimentary Delivery Routing
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Solution Overview
Problem
Existing methods for path selection using route guidance suffer from inaccuracy in predictions and are not well-suited for real-time updates, which affects the efficiency of alimentary combinations delivery.
Innovation Solution
A system and method that utilize a computing device to compute projected alimentary combinations based on expected completion time and destination geolocation, employing a batching objective function to rank candidate combinations and provide updated predicted paths using machine-learning processes for efficient delivery routing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods for path selection using route guidance are used, then the system can provide delivery routing, but the predictions are inaccurate and not well-suited for real-time updates
Solution Approach 1:
The patent implements dynamic path selection by continuously updating predictions as alimentary elements reach their destinations. The system transitions from static route guidance to a dynamic model that adapts in real-time based on actual delivery progress, allowing the predicted path to be recalculated and optimized during the delivery process rather than being fixed in advance.
Solution Approach 2:
The system incorporates feedback mechanisms where actual delivery status information is fed back into the prediction model. As each alimentary element reaches its destination, the system uses this feedback to update and refine future predictions, improving accuracy over time and enabling real-time optimization of delivery routes based on actual performance data.
2Productivity
If a batching objective function is used to rank candidate combinations, then delivery efficiency is optimized, but the system complexity increases
Solution Approach 1:
The patent segments the delivery system into discrete batching combinations, where alimentary elements are grouped into manageable batches that can be processed and delivered independently. This segmentation allows the complex optimization problem to be broken down into smaller, more manageable units that can be ranked and processed systematically using the batching objective function.
Solution Approach 2:
The system uses parameter changes by transforming the delivery optimization problem into a mathematical formulation with objective functions and constraints. By defining the batching objective function with specific parameters (such as delivery time, distance, and resource utilization), the system converts complex real-world delivery challenges into solvable mathematical models that can be optimized efficiently.
Data Source
AI summary
A system for path selection, the system comprising a computing device, wherein the computing device is configured to receive a plurality of alimentary elements and a plurality of destinations. Computing device may compute, using the plurality of alimentary elements and the plurality of destinations, a projected combination as a function of an objective function, wherein computing is based on completion time and destination. Computing device may determine a combination ranking by generating a batching objective function, wherein the function generates an output ranking according to at least a target criterion and selects a combination. Computing device may provide batching instructions to a user. Computing device may determine a predicted path for the plurality of alimentary elements wherein the predicted path is updated as a function of each alimentary element that has reached its destination. Computing device may provide the predicted path to a user.