Ensemble ML for Operational Load Balancing
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Solution Overview
Problem
Existing operational load balancing systems face inefficiencies due to high computational complexity and storage requirements, as well as reliability issues stemming from the need to process all possible operator-unit mapping arrangements, rather than filtered subsets that satisfy specific constraints.
Innovation Solution
The method decouples constraint enforcement from optimization operations by determining constraint-satisfying operator-unit mapping arrangements that meet operator unity and capacity constraints, using an optimization-based ensemble machine learning model to find an optimal mapping, thereby reducing computational and storage burdens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all possible operator-unit mapping arrangements are processed to ensure optimal load balancing, then reliability is improved, but computational complexity and storage requirements increase significantly
Solution Approach 1:
The patent segments the processing task by dividing all possible operator-unit mapping arrangements into two distinct groups: constraint-satisfying arrangements and constraint-violating arrangements. The system processes only the constraint-satisfying subset through the ensemble machine learning model, while eliminating the need to process constraint-violating arrangements. This segmentation reduces computational complexity and storage requirements while maintaining reliability by ensuring that only valid mappings are considered for load balancing decisions.
2Reliability
If all possible operator-unit mapping arrangements are processed to ensure optimal load balancing, then reliability is improved, but storage requirements increase significantly
Solution Approach 1:
The patent extracts and isolates only the constraint-satisfying operator-unit mapping arrangements from the complete set of all possible arrangements. By using constraint satisfaction techniques to identify and extract this valid subset beforehand, the system eliminates the need to store and process constraint-violating arrangements. This extraction approach significantly reduces storage requirements while maintaining the reliability needed for optimal load balancing, as the ensemble model only needs to evaluate valid mappings.
3Measurement precision
If priority scores are generated for all work unit profiles to ensure comprehensive evaluation, then measurement precision is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-determining the constraint-satisfying operator-unit mapping arrangements before generating priority scores. By establishing the valid mapping subset in advance using constraint satisfaction techniques, the system enables the ensemble machine learning model to generate priority scores only for work unit profiles within this validated subset. This preliminary filtering action maintains measurement precision for work unit evaluation while significantly reducing computational complexity by eliminating unnecessary score generation for invalid mappings.
Data Source
AI summary
There is a need for more effective and efficient constrained-optimization-based operational load balancing. In one example, a method comprises determining constraint-satisfying operator-unit mapping arrangements that satisfy an operator unity constraint and an operator capacity constraint; for each constraint-satisfying operator-unit mapping arrangement, determining an arrangement utility measure; processing each arrangement utility measure using an optimization-based ensemble machine learning model that is configured to determine an optimal operator-unit mapping arrangement of the plurality of constraint-satisfying operator-unit mapping arrangements; and initiating the performance of one or more operational load balancing operations based on the optimal operator-unit mapping arrangement.