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Production scheduling is critical for manufacturing system. Dispatching rules are usually applied dynamically to schedule the job in the dynamic job-shop. The paper presents an adaptive iterative scheduling algorithm that operates dynamically to schedule the job in the dynamic job-shop. In order to get adaptive behavior, the reinforcement learning system is done with the phased Q-learning by defining...
The paper presents an adaptive iterative distributed scheduling algorithm that operates dynamically to schedule the job in the dynamic job-shop. The manufacturing system is scheduled by the multi-agent system where every machine and job is associated with its own software agent. Each agent learns how to select presumably good schedules, by this way the size of the search space can be reduced. In order...
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