ai4co/parco
[NeurIPS 2025] PARCO: Parallel AutoRegressive Combinatorial Optimization
This project helps operations managers and logistics planners quickly find optimal solutions for complex allocation and scheduling challenges. It takes in details about your delivery routes, vehicle capacities, or factory production steps, and outputs efficient plans for vehicle assignments, pickup/delivery sequences, or job scheduling. This is ideal for professionals needing to optimize resources in logistics, manufacturing, or supply chain management.
Use this if you need to solve complex combinatorial optimization problems like vehicle routing or job scheduling efficiently, especially when dealing with multiple interdependent agents or resources.
Not ideal if your optimization problems are simple, static, or if you prefer traditional solvers without machine learning components.
Stars
44
Forks
4
Language
Python
License
MIT
Category
Last pushed
Dec 03, 2025
Commits (30d)
0
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