Graph-Based Order Forecasting for Dynamic Agent Deployment
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Solution Overview
Problem
Existing systems struggle to efficiently manage order processing resources by accurately forecasting future order volumes, leading to potential shortages or excesses in agent deployment.
Innovation Solution
A system utilizing generative modeling and graph structure analysis on time series datasets to generate forecasting models, which are then used to optimize agent deployment based on predicted order volumes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional forecasting methods are used for order volumes, then simplicity and ease of implementation are maintained, but forecasting accuracy and reliability deteriorate
Solution Approach 1:
The patent segments the order forecasting problem into multiple independent time series datasets, each representing different order categories or time periods. Graph structures are constructed for each segment separately, allowing specialized modeling for each segment while maintaining overall system manageability. This segmentation enables accurate forecasting for each segment without requiring a single overly complex monolithic model.
Solution Approach 2:
The patent introduces graph structures as intermediary representations between raw order data and forecasting models. These graph structures serve as mediators that capture complex relationships and patterns in the data, enabling accurate forecasting while keeping the underlying model architecture relatively simple. The graph structures act as an intermediate layer that transforms raw data into meaningful patterns.
2Reliability
If more agents are deployed to handle potential order volume increases, then service reliability is improved, but resource utilization efficiency deteriorates due to excess capacity during low-volume periods
Solution Approach 1:
The patent implements dynamic agent deployment based on forecasted order volumes. Instead of static staffing levels, the system continuously updates forecasts using the graph-based models and adjusts agent allocation accordingly. This dynamic approach ensures sufficient agents are available during high-volume periods while reducing excess capacity during low-volume periods, maintaining service reliability while improving overall resource utilization efficiency.
Solution Approach 2:
The patent performs preliminary forecasting of order volumes using the graph structure models before deploying agents. By predicting future order volumes in advance, the system can proactively allocate the appropriate number of agents before demand occurs, ensuring service reliability is maintained while avoiding the inefficiency of having excess agents idle during low-demand periods.
3Measurement precision
If granular-level forecasting is implemented for diverse order types, then forecasting precision is improved, but computational complexity and data processing requirements worsen
Solution Approach 1:
The patent applies local quality by constructing specialized graph structures for each granular order category or time series dataset. Each graph structure is tailored to capture the specific patterns and relationships relevant to that particular category, enabling high-precision forecasting for each local segment. This approach allows the system to achieve granular-level forecasting precision without requiring a single overly complex model that would need to handle all diversity simultaneously.
Data Source
AI summary
A method for managing order processing includes obtaining a set of time series datasets associated with the order processing for an order processing system, generating an adjacency matrix using the set of time series datasets and using a recurrent neural network (RNN), applying a graphical Fourier transformation on the adjacency matrix using a Laplacian matrix and an inverse graph Fourier to obtain a graph Fourier transform, applying a sequential network on the graph Fourier transform using a fast Fourier transform network and a convolution layer to obtain output features, performing a generative modeling on the output features to generate a forecasting sequence, and initiating an agent deployment of a plurality of agents on the order processing system based on the forecasting sequence, wherein the plurality of agents each provide services associated with order processing.