[Feat] Add JSSP environment

LTluttmann commented 3 months ago

Description

Added environment for the Job-Shop Scheduling Problem (JSSP). This implementation of JSSP treats JSSP as a special case of FJSP, where each operation can be processed by only one machine. As such, the environment is implemented as subclass of the FJSPEnv, only changing the action space (action space reduces to the selection of next job to execute) and the data generator.
In addition, the HetGNN policy has been restructured and renamed to L2D (for learning to dispatch) and is now applicable to both, FJSP and JSSP.

Motivation and Context

JSSP is a common CO problem and a widely used problem to benchmark new algorithms
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Types of changes

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[ ] Bug fix (non-breaking change which fixes an issue)
[x] New feature (non-breaking change which adds core functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to change)
[ ] Documentation (update in the documentation)
[ ] Example (update in the folder of examples)

Checklist

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[ ] My change requires a change to the documentation.
[x] I have updated the tests accordingly (required for a bug fix or a new feature).
[ ] I have updated the documentation accordingly.

fedebotu commented 3 months ago

@Junyoungpark tagging you since you know this problem very well!

Do you think we have a chance to include ScheduleNet?

LTluttmann commented 3 months ago

Thanks for reviewing guys. I will add a ton of changes here in a couple of minutes and I hope this PR will not get too messy. Let me know if we should go through it together.

Additional changes:

stepwise PPO for L2D
Some MatNet changes to better work for JSSP / FJSP
Running mean / variance class for reward / advantage scaling
stepwise L2D policy
attention based models for fjsp / jssp
minor bugfixes and improvements here and there

fedebotu commented 3 months ago

Wow, lots of changes here! 😁 Really curious about episodic / stepwise RL performances

Btw, feel free to merge anytime

fedebotu commented 3 months ago

Great job!! This PR is truly huge

ai4co / rl4co