Scheduling Policy Adaptation with Importance Sampling
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Solution Overview
Problem
Manufacturing facilities face challenges in efficiently producing diverse product lots due to misalignment between training and deployment scenarios, limited computational resources, and strict deployment time limits, leading to inadequate scheduling policy adaptations.
Innovation Solution
A method and system for policy adaptation using importance sampling to generate improved scheduling policies on industrial computers with limited resources within tight time frames, by analyzing backtest cases and updating policies based on first and second state distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If reinforcement learning is used to train scheduling policies on simulators with historic datasets, then scheduling performance may be improved, but misalignment between training data and deployment scenarios significantly reduces system performance
Solution Approach 1:
The patent changes the parameter of state distribution by computing importance weights that transform the training state distribution to match the deployment state distribution. This allows the policy to adapt to different scenarios by adjusting the weighting of states based on their actual occurrence frequency in deployment environments.
Solution Approach 2:
The patent implements feedback by using importance sampling to compare training state distributions with deployment state distributions. The importance weights provide feedback signals that guide policy adaptation, allowing the system to learn from the mismatch between training and deployment scenarios.
2Ease of manufacture
If policy adaptation is performed using specialized processors like GPUs, then training effectiveness is improved, but industrial computers with general purpose processors have limited computational power
Solution Approach 1:
The patent extracts the computationally intensive training process from the deployment environment. By performing training on specialized processors and only adapting policies on industrial computers using importance sampling, it separates the heavy computation from the resource-constrained deployment system.
Solution Approach 2:
The patent performs preliminary training actions on powerful processors before deployment. The policy is pre-trained on historic data using available computational resources, and then adapted to deployment scenarios using efficient importance sampling that requires minimal computational power.
3Speed
If strict deployment time limits are enforced with time limits of several minutes, then deployment speed is improved, but training and adaptation must be completed within very restricted time frames
Solution Approach 1:
The patent performs partial adaptation by focusing only on the most critical updates needed for deployment. Instead of complete retraining, it applies importance sampling to make targeted adjustments to the policy, achieving sufficient adaptation within tight time constraints.
Solution Approach 2:
The patent rushes through the adaptation process by using importance sampling, which requires significantly fewer computational steps than full reinforcement learning training. This allows the system to quickly adjust policies to match deployment scenarios within minute-level time limits.
Data Source
AI summary
A system and a method are disclosed for scheduling, the method includes determining a second state distribution based on executing a first scheduling policy in a second environment, generating a second scheduling policy based on a first policy gradient associated with a first state distribution and the second state distribution, the first state distribution being associated with the first scheduling policy and a first environment, different from the second environment, and controlling a scheduling process based on the second scheduling policy.