ML Routing Prediction for Resource Allocation Efficiency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing routing processes are complex and challenging to determine, especially when new processes need to be arranged or existing ones modified, due to the numerous variables involved in processing resources, operations, and their sequencing, leading to inefficiencies and suboptimal outcomes.
Innovation Solution
The use of machine learning models trained with historical data to predict processing resources, operation sequences, and standard values associated with operations, enabling the determination of optimal routing configurations by analyzing inference data and generating probability models for sequencing and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to determine routing processes, then flexibility and adaptability are maintained, but the complexity and time required increase significantly
Solution Approach 1:
The patent replaces manual routing determination methods with machine learning models that automatically predict processing resources, operations, and sequences. The system uses trained models to analyze input data and generate routing recommendations, substituting human expertise with automated intelligent systems that reduce complexity while maintaining or improving efficiency
Solution Approach 2:
The system performs preliminary actions by pre-training machine learning models with historical routing data before actual routing determination is needed. The models are prepared in advance with knowledge from past processes, enabling them to quickly predict optimal routing configurations without requiring complex real-time analysis
2Measurement precision
If comprehensive historical data is used for training, then prediction accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The patent segments the training process into distinct phases: data collection, data preprocessing, model training, and validation. By dividing the comprehensive historical data into manageable subsets and training models iteratively, the system achieves high prediction accuracy without requiring all data to be processed simultaneously, reducing overall training time
Solution Approach 2:
The system uses partial action by selecting only the most relevant features and data points from historical records for training, rather than processing every available detail. This selective approach maintains prediction accuracy while significantly reducing the computational burden and training time
Data Source
AI summary
Techniques and solutions are provided for predicting elements of a routing. Such elements include processing resources used in processing a set of inputs, a sequence of processing resources used in processing a set of inputs, operations performed on the inputs, a sequence of the operations, standard values associated with the operations, and how inputs are allocated to processing resources or operations. A machine learning model is trained with a set of inputs and a set of labels for one or more elements of a routing. A set of inputs for inference data is provided to the trained model and a prediction for one of the routing elements is provided. For sequence information, training data can be used to generate a probability model which can be used to obtain an inferred sequence of processing resources or operations.