Density Map Generation for Resource Positioning
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Solution Overview
Problem
In transportation and logistics, determining the optimal positioning of resources such as delivery vehicles is time-intensive and costly, especially when considering factors like weather conditions and seasonal events, which existing methods fail to address effectively.
Innovation Solution
A method involving the analysis of transaction data to train models that generate density maps indicating future resource requirements, taking into account parameters like date, time, public holidays, and weather conditions, to determine the most efficient locations for resource placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used to position delivery vehicles, then resources can be placed in locations, but the process is time-intensive and costly
Solution Approach 1:
The system performs preliminary analysis of transaction data to generate density maps that predict future resource requirements in advance. By pre-processing historical data and creating predictive models, the system eliminates the need for time-intensive manual positioning decisions when resources are needed, allowing rapid deployment based on pre-computed density maps.
Solution Approach 2:
The patent replaces manual, mechanical decision-making processes with an automated computational system. Machine learning models analyze transaction data and generate density maps automatically, substituting human judgment and manual planning with algorithmic processing that is both faster and more consistent.
2Productivity
If traditional methods are used to position delivery vehicles, then resources can be placed in locations, but the process is costly
Solution Approach 1:
The system uses historical transaction data from the logistics network itself to generate the density maps, rather than requiring external consulting or manual analysis. The data already exists within the system, and the machine learning models process this internal data autonomously, eliminating the need for expensive external resources or manual expert analysis.
Solution Approach 2:
The patent replaces expensive manual processes with automated computational algorithms. Once the machine learning models are trained, they can rapidly generate density maps at minimal computational cost, replacing costly human expert analysis and manual planning processes.
3Adaptability or versatility
If existing methods are used, then resource positioning can be determined, but weather conditions and seasonal events are not effectively addressed
Solution Approach 1:
The system incorporates additional parameters such as weather conditions, seasonal events, and temporal factors into the machine learning models. By training models on transaction data that includes these varying conditions, the system learns to adapt its predictions to different environmental and temporal contexts, improving both adaptability and prediction accuracy simultaneously.
Solution Approach 2:
The system pre-trains machine learning models on historical transaction data that encompasses various weather conditions and seasonal events. This preliminary training allows the models to learn patterns and adaptations to different conditions in advance, so when new resource positioning decisions are needed, the models can quickly provide accurate predictions that already account for current weather and seasonal factors.
Data Source
AI summary
A method (200) for determining future resource requirements in a given location, the method comprising the steps of obtaining (110) transaction data, the transaction data indicative of past resource use at a given location. At least one model may be trained (120) based on a set of parameters by determining (121) a distribution associated with the transaction data; and generating (122) a kernel based on the distribution and the set parameters, the kernel being arranged to output an estimated distribution. The kernel is refined (123) and validated (124) based on a comparison between the distribution and the estimated distribution. A density map is then generated (125) based on the at least one trained model. The density map is then sent (130) to a control system for determining future resource requirements in the given location.