* Notice that this article was written using a translator
Hello, I am a developer and project manager working for a Korean manufacturing company.
Recently, while working on flexsim-related tasks, I became interested in using flexsim and reinforcement learning together.
In the flexsim example, I followed the example using gym, and when I was trying to figure out how to apply it as a real job, I got a question.
First, I wonder if my understanding is correct. My understanding of the content of tutorial using gym was as follows.
-The purpose is to create an artificial intelligence model(RL) that determines which item to send to the processor among the items (1~5) accumulated in the queue to increase the throughput the most.
I think that simulation, which I understand, is used to verify in advance if there is a wrong process before applying a real world problem.
However, I wondered what problems in reality would be solved by making the strategy of transferring from Queue to Processor in the simulation into a reinforcement learning model.
So.
1. I wonder what kind of value in real world we can develop into by developing the tutorial
2. What keywords are there to look for problems similar to optimization problems that Gym tutorial wants to solve
ex/ combination optimization, job shop scheduling... etc
3. Would you like to develop more examples of reinforcement learning with flexsim?
p.s. I'm very excited about FlexSim's reinforcement learning capabilities, and I'm looking forward to it. I hope that content related to reinforcement learning will continue to be added in the next version : ) thx for reading
