Bipartite Graph Neural Network for Task Allocation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The allocation of tasks to staff members in facilities like retailers and warehouses is computationally challenging due to the variety of tasks and staff, leading to suboptimal resource utilization and task performance.
Innovation Solution
A system utilizing a computing device that generates a bipartite sub-graph based on task and worker attributes, employing a graph neural network to score edges and allocate tasks optimally, while mitigating network biases and allowing for system reconfiguration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional task allocation methods are used, then the system is simple to implement, but the task allocation efficiency and resource utilization are suboptimal
Solution Approach 1:
The patent replaces traditional mechanical/manual task allocation methods with an automated neural network-based system. The bipartite graph neural network automatically processes task and worker attributes, generates scores, and allocates tasks optimally without manual intervention, thereby improving allocation efficiency while accepting increased system complexity through automation.
Solution Approach 2:
The patent transforms the task allocation problem into a parameter-based scoring system. By encoding task attributes and worker attributes as vectors and computing edge scores based on these parameters, the system enables quantitative optimization of task allocation, improving productivity through data-driven decision-making.
2Manufacturing precision
If comprehensive task and worker attributes are considered, then the task allocation optimality is improved, but the computational complexity increases
Solution Approach 1:
The patent segments the comprehensive task allocation problem into manageable components: task attribute encoding, worker attribute encoding, edge score computation, and task assignment. The bipartite graph structure divides workers and tasks into separate node sets, allowing systematic processing of multiple attributes without overwhelming computational complexity.
Solution Approach 2:
The patent introduces edge scores as an intermediary metric that mediates between task attributes and worker attributes. Instead of directly comparing complex multi-attribute profiles, the system computes intermediate edge scores that capture the compatibility between tasks and workers, simplifying the optimization process while maintaining allocation optimality.
3Measurement precision
If neural network is used for scoring, then the allocation accuracy is improved, but the training time and computational resources increase
Solution Approach 1:
The patent performs preliminary action by training the bipartite graph neural network offline before deployment. The model learns optimal scoring patterns from historical task and worker data, and once trained, can rapidly score new task-worker pairs during operation. This separates the time-consuming training phase from the real-time allocation phase, improving scoring accuracy while minimizing impact on operational time.
Data Source
AI summary
A method includes: obtaining task records defining tasks to be performed, and worker profiles corresponding to workers to perform the tasks; generating a bipartite sub-graph including: source nodes for the task records, each having a source feature vector encoding task attributes corresponding to the task record, a target node having a target feature vector encoding worker attributes corresponding to a first one of the worker profiles, and a set of edges connecting each source node with the target node, each edge having an edge feature vector derived by comparing the task attributes with the worker attributes; generating, via execution of a graph neural network, scores corresponding to the edges; based on the scores, allocating a first task to the first worker profile; and transmitting the task record corresponding to the first task to a client computing device corresponding to the first worker profile.