Datasets:

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@@ -63,18 +63,31 @@ Results compare algorithm performance across different problem sizes:
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  ```python
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  # Example of loading a dataset
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- from load_dataset import load_vrp_dataset
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-
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- # Load a CVRP dataset with 50 customers, single depot configuration
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- dataset = load_vrp_dataset('cvrp', 50, 'single_depot')
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-
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- # Run evaluation
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- from vrp_bench.vrp_evaluation import VRPEvaluator
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- evaluator = VRPEvaluator()
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- results = evaluator.evaluate_solver(solver_class=ACOSolver,
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- solver_name="ACO",
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- sizes=[50, 100])
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ```
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  ## 🔑 Features
 
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  ```python
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  # Example of loading a dataset
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+ from datasets import load_dataset
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+ ds = load_dataset("MBZUAI/svrp-bench", split="test")
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+ ds[0]
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+ ```
 
 
 
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+ ### Sample
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+ ```json
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+ {'appear_times': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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+ 'demands': [0, 33, 52, 35, 85, 77, 68, 17, 61, 32, 23],
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+ 'file_name': 'cvrp_10_multi_depot_multi_vehicule_capacities.npz',
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+ 'instance_id': 0,
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+ 'locations': [[523, 497],
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+ [394, 344],
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+ [536, 599],
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+ [341, 412],
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+ [734, 652],
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+ [492, 569],
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+ [491, 238],
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+ [419, 787],
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+ [688, 422],
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+ [708, 490],
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+ [431, 454]],
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+ 'num_vehicles': 13,
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+ 'subset_name': 'cvrp_10_multi_depot_multi_vehicule_capacities',
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+ 'vehicle_capacities': [40.0]}
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  ```
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  ## 🔑 Features