Factory Dispatching with Reinforcement Learning for Adaptive Job Scheduling
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Solution Overview
Problem
Conventional heuristic methods for scheduling in factories require human intervention, limiting operational efficiency and flexibility in managing multiple objectives across various equipment, necessitating a more adaptive and efficient dispatching method.
Innovation Solution
A dispatching method and apparatus based on reinforcement learning that constructs a Markov decision process (MDP) for dispatching actions and rewards, applying reinforcement learning to select jobs that maximize a weighted sum of dispatching scores, allowing for adaptive application in complex factory environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If heuristic solution methods based on human-tuned parameters are used for scheduling, then operational schedules can be established for factory equipment, but human intervention is required every time which limits operational efficiency
Solution Approach 1:
The system uses reinforcement learning to enable the dispatching system to automatically learn and optimize scheduling policies without continuous human intervention. The RL agent autonomously improves dispatching decisions by learning from environmental feedback, making the system self-improving and reducing dependency on manual tuning.
Solution Approach 2:
The invention transitions from static human-tuned parameters to dynamic parameters learned through reinforcement learning. The dispatching policy parameters are continuously updated based on environmental states and rewards, allowing the system to adapt to changing factory conditions automatically.
2Adaptability or versatility
If heuristic methods are used for dispatching policy, then schedules can be created, but the methods have limitations in establishing effective dispatching policies for selecting and ordering jobs
Solution Approach 1:
The reinforcement learning framework implements continuous feedback loops where the dispatching policy is evaluated based on rewards from environmental outcomes. This feedback mechanism allows the system to learn effective dispatching policies by continuously adjusting based on performance results, overcoming the limitations of static heuristic methods.
Solution Approach 2:
The dispatching policy transitions from static heuristic rules to dynamic policies that adapt based on environmental states. The RL agent learns to select and order jobs dynamically according to current factory conditions, making the dispatching system flexible and responsive to changing circumstances.
3Productivity
If reinforcement learning is applied to construct MDP for dispatching actions, then jobs can be selected to maximize weighted sum of dispatching scores, but the system complexity increases
Solution Approach 1:
The reinforcement learning system is segmented into distinct functional components: MDP construction unit, RL application unit, and job selection unit. This modular architecture manages system complexity by dividing the complex RL implementation into manageable, independent modules that can be developed and maintained separately.
Solution Approach 2:
The Markov decision process serves as an intermediary framework that bridges the gap between complex reinforcement learning algorithms and practical job selection decisions. The MDP formalizes the decision-making process into structured states, actions, and rewards, making the RL system more manageable and interpretable.
Data Source
AI summary
Provided is a dispatching method in a factory based on reinforcement learning. The dispatching method in a factory based on reinforcement learning may comprise: constructing a Markov decision process (MDP) for dispatching actions of a dispatcher in the factory and resulting rewards and states of the factory; performing learning by applying reinforcement learning (RL) to the constructed MDP; and as a result of said RL, selecting a job that maximizes a weighted sum of a plurality of scored dispatching rules.