The implementation of model AMO-ACO - Alimentation Deep Multiple Optimal Ant Colony Optimization to solve Vehicle Routing Problem with Time Windows.
We ultilize a novel model by combining both constraints from CVRP and TW problems to generate heuristic measures to strike a balance between vehicle utilization, route optimization, and adherence to time windows through effective combination strategies.
More detail: Alimentation Deep Multiple Optimal Ant Colony Optimization to solve Vehicle Routing Problem with Time Windows.
Move to Model/train.py, change:
cfg = Data_{}().format(size of graph you want)
train(cfg, cfg.graph_size, cfg.n_ants, cfg.steps_per_epoch, cfg.capacity, cfg.epochs)
cfg is the config data, which are set up for hyperparameters for 100,200 and 400 CVRPTW dataset.
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