ML Ensemble for Dynamic Resource Allocation
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Solution Overview
Problem
Current machine learning models struggle with dynamically allocating resources between central and remote locations due to changing parameters over time, leading to suboptimal resource allocation and difficulty in meeting multiple constraints, especially in complex environments with a large number of possible allocations.
Innovation Solution
An ensemble of machine learning models, including a central ML model, remote ML models, and a main ML model, is trained on specific datasets for each location, using historical data and manual corrections to predict optimal resource allocation across locations, with interactions between models to adapt to dynamic changes and satisfy constraints such as social distancing requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional machine learning models are used for resource allocation, then the system is simpler to implement, but the allocation performance deteriorates in dynamic environments with changing parameters
Solution Approach 1:
The system divides the resource allocation problem into multiple specialized models: a central ML model for overall coordination, remote ML models for location-specific decisions, and a main ML model for optimization. Each model segment handles specific aspects of the allocation problem, improving overall performance while managing complexity through functional decomposition.
Solution Approach 2:
The ensemble of ML models is designed to dynamically adapt to changing parameters in the environment. The models continuously learn from new data and adjust their predictions, enabling the system to respond to dynamic conditions such as changing resource demands, location parameters, and constraints, thereby maintaining high allocation performance.
2Measurement precision
If an ensemble of ML models is used to meet multiple constraints, then the allocation accuracy improves, but the computational complexity increases
Solution Approach 1:
The computational workload is segmented across multiple specialized models rather than concentrated in a single complex model. The central model handles high-level coordination, remote models handle location-specific constraints, and the main model performs optimization, distributing computational complexity while maintaining high allocation accuracy through collaborative decision-making.
Solution Approach 2:
The central ML model acts as an intermediary that coordinates between remote ML models and the main ML model. It aggregates information from remote models, incorporates global constraints, and guides the optimization process, thereby managing computational complexity through hierarchical mediation while preserving allocation accuracy.
3Reliability
If manual corrections are incorporated into training data, then the model reliability improves, but the training process complexity increases
Solution Approach 1:
Manual corrections are incorporated as feedback into the training process. The system collects expert feedback on model predictions, integrates these corrections into the training data, and retrains the models. This feedback loop continuously improves model reliability by learning from human expertise while managing training complexity through systematic integration of corrections.
Solution Approach 2:
Manual corrections and expert knowledge are incorporated into the training data beforehand to pre-condition the models for reliable performance. By preparing the training data with curated corrections in advance, the system establishes a solid foundation for model reliability without requiring complex real-time adjustments during deployment.
Data Source
AI summary
There is provided a method for training a ML model ensemble, comprising: training a central ML model using training values of central location parameters of a central location, for sample resources: training a respective ML model using the remote location parameters for the respective sample resource, receiving a manual indication of allocation of the respective sample resource to the central location or the remote location, creating a main training dataset including the training values for the central locations, and the training values for the remote location parameters for each of the sample resources labelled with the manual indication, and training a main ML model using the training dataset for allocating a resource to central location(s) or to the respective remote location, wherein a subset of resources are allocated to respective remote locations and another subset of the resources are allocated to central location(s).