Ren-Research/LOMAR
[ICML 2023] Learning for Edge-Weighted Online Bipartite Matching with Robustness Guarantees
This project helps operations managers, resource allocators, or platform administrators make optimal real-time decisions when matching incoming requests with available resources. For example, matching ride-share drivers to passengers, or delivery personnel to orders, where the 'edges' of the match have different values. It takes as input data representing available resources and incoming requests with their associated 'weights' or values, and outputs the best real-time match to maximize overall reward or efficiency.
No commits in the last 6 months.
Use this if you need to quickly and robustly match incoming requests to available resources, where each potential match has a specific value, and you want to maximize the total value of matches over time.
Not ideal if your matching problem is static and all requests and resources are known beforehand, or if you need an exact optimal solution rather than a robust, high-performing approximation.
Stars
11
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1
Language
Python
License
MIT
Category
Last pushed
Aug 09, 2023
Commits (30d)
0
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