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Official implementation of IJCAI'24 paper "Towards Generalizable Neural Solvers for Vehicle Routing Problems via Ensemble with Transferrable Local Policy"

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ELG (Ensemble of Local and Global policies)

This repository is the code of the https://arxiv.org/abs/2308.14104, which ensembles a transferrable local policy to boost generalization. We provide the trained models to reproduce the test results in the paper.

Test ELG-POMO on VRPLIB

Under the ELG/CVRP folder, use the default settings in config.yml, run

python test_vrplib.py

You can choose the vrplib_set config from {X, XXL} to test on two different VRPLIB sets.

Test ELG-POMO on TSPLIB

Under the ELG/TSP folder, use the default settings in config.yml, and run

python test_tsplib.py

Train ELG-POMO on CVRP or TSP

First, generate the validation sets by

python generate_data.py

Modify the load_checkpoint config in config.yml to Null (i.e., load_checkpoint: ), and run

python train.py

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Official implementation of IJCAI'24 paper "Towards Generalizable Neural Solvers for Vehicle Routing Problems via Ensemble with Transferrable Local Policy"

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