nsidn98/NICE
Combining Reinforcement Learning with Integer Programming for Robust Scheduling
This helps operations managers and planners create robust schedules, particularly for complex scenarios like airline crew assignments, that can better handle unexpected disruptions like flight delays. It takes in operational constraints and scheduling goals and produces a crew schedule that is less impacted by unforeseen events, performing better than traditional methods and often faster than other robust approaches. This is for professionals managing complex scheduling in domains where disruptions are common and costly.
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Use this if you need to create schedules that can resiliently absorb real-world disruptions with less manual intervention and lower computational overhead than existing robust scheduling techniques.
Not ideal if your scheduling problems are simple, rarely encounter disruptions, or do not require highly optimized robustness to unexpected events.
Stars
30
Forks
10
Language
Python
License
GPL-3.0
Category
Last pushed
Feb 17, 2024
Commits (30d)
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