Aria AI Operations Research Portfolio
Collection
Enterprise OR, optimization, and decomposition demos by Aria AI • 136 items • Updated
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
name: string
version: string
source_reference: string
records: int64
feature_groups: list<item: string>
child 0, item: string
samples: list<item: struct<instance_id: string, size: string, recommended_solver: string, confidence: double, (... 34 chars omitted)
child 0, item: struct<instance_id: string, size: string, recommended_solver: string, confidence: double, predicted_ (... 22 chars omitted)
child 0, instance_id: string
child 1, size: string
child 2, recommended_solver: string
child 3, confidence: double
child 4, predicted_objective_30s: double
instance_id: string
label: string
features: struct<fjssp: struct<n_jobs: int64, n_machines: int64, n_operations: int64, machine_flexibility: dou (... 293 chars omitted)
child 0, fjssp: struct<n_jobs: int64, n_machines: int64, n_operations: int64, machine_flexibility: double, processin (... 133 chars omitted)
child 0, n_jobs: int64
child 1, n_machines: int64
child 2, n_operations: int64
child 3, machine_flexibility: double
child 4, processing_time_variance: double
child 5, workload_skew: double
child 6, precedence_density: double
child 7, alternative_machine_ratio: double
child 8, setup_density: double
child 1, graph: struct<graph_density: double, clustering_coefficient: double, avg_degree: double, connected_componen (... 36 chars omitted)
child 0, graph_density: double
child 1, clustering_coefficient: double
child 2, avg_deg
...
_sec: double
child 7, rationale: string
child 8, ensemble_backend: string
child 9, rankings: list<item: struct<solver_id: string, score: double, predicted_runtime_sec: double, predicted_objecti (... 447 chars omitted)
child 0, item: struct<solver_id: string, score: double, predicted_runtime_sec: double, predicted_objective_5s: doub (... 435 chars omitted)
child 0, solver_id: string
child 1, score: double
child 2, predicted_runtime_sec: double
child 3, predicted_objective_5s: double
child 4, predicted_objective_30s: double
child 5, optimality_probability: double
child 6, config: struct<time_limit_sec: int64, num_search_workers: int64, presolve: bool, search_branching: string, h (... 257 chars omitted)
child 0, time_limit_sec: int64
child 1, num_search_workers: int64
child 2, presolve: bool
child 3, search_branching: string
child 4, heuristic_frequency: double
child 5, population_size: int64
child 6, mutation_rate: double
child 7, crossover_rate: double
child 8, solver: string
child 9, neighborhood_size: int64
child 10, destroy_pct: double
child 11, initial_solution: string
child 12, presolving: bool
child 13, separating: bool
child 14, heuristics: bool
child 15, threads: int64
to
{'instance_id': Value('string'), 'size': Value('string'), 'seed': Value('int64'), 'label': Value('string'), 'features': {'fjssp': {'n_jobs': Value('int64'), 'n_machines': Value('int64'), 'n_operations': Value('int64'), 'machine_flexibility': Value('float64'), 'processing_time_variance': Value('float64'), 'workload_skew': Value('float64'), 'precedence_density': Value('float64'), 'alternative_machine_ratio': Value('float64'), 'setup_density': Value('float64')}, 'graph': {'graph_density': Value('float64'), 'clustering_coefficient': Value('float64'), 'avg_degree': Value('float64'), 'connected_components': Value('int64'), 'treewidth_approx': Value('float64')}}, 'feature_vector': List(Value('float64')), 'prediction': {'recommended_solver': Value('string'), 'recommended_config': {'time_limit_sec': Value('int64'), 'num_search_workers': Value('int64'), 'presolve': Value('bool'), 'search_branching': Value('string'), 'heuristic_frequency': Value('float64')}, 'confidence': Value('float64'), 'predicted_objective_5s': Value('float64'), 'predicted_objective_30s': Value('float64'), 'optimality_probability': Value('float64'), 'predicted_runtime_sec': Value('float64'), 'rationale': Value('string'), 'ensemble_backend': Value('string'), 'rankings': List({'solver_id': Value('string'), 'score': Value('float64'), 'predicted_runtime_sec': Value('float64'), 'predicted_objective_5s': Value('float64'), 'predicted_objective_30s': Value('float64'), 'optimality_probability': Value('float64'), 'config': {'time_limit_sec': Value('int64'), 'num_search_workers': Value('int64'), 'presolve': Value('bool'), 'search_branching': Value('string'), 'heuristic_frequency': Value('float64'), 'population_size': Value('int64'), 'mutation_rate': Value('float64'), 'crossover_rate': Value('float64'), 'solver': Value('string'), 'neighborhood_size': Value('int64'), 'destroy_pct': Value('float64'), 'initial_solution': Value('string'), 'presolving': Value('bool'), 'separating': Value('bool'), 'heuristics': Value('bool'), 'threads': Value('int64')}})}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
name: string
version: string
source_reference: string
records: int64
feature_groups: list<item: string>
child 0, item: string
samples: list<item: struct<instance_id: string, size: string, recommended_solver: string, confidence: double, (... 34 chars omitted)
child 0, item: struct<instance_id: string, size: string, recommended_solver: string, confidence: double, predicted_ (... 22 chars omitted)
child 0, instance_id: string
child 1, size: string
child 2, recommended_solver: string
child 3, confidence: double
child 4, predicted_objective_30s: double
instance_id: string
label: string
features: struct<fjssp: struct<n_jobs: int64, n_machines: int64, n_operations: int64, machine_flexibility: dou (... 293 chars omitted)
child 0, fjssp: struct<n_jobs: int64, n_machines: int64, n_operations: int64, machine_flexibility: double, processin (... 133 chars omitted)
child 0, n_jobs: int64
child 1, n_machines: int64
child 2, n_operations: int64
child 3, machine_flexibility: double
child 4, processing_time_variance: double
child 5, workload_skew: double
child 6, precedence_density: double
child 7, alternative_machine_ratio: double
child 8, setup_density: double
child 1, graph: struct<graph_density: double, clustering_coefficient: double, avg_degree: double, connected_componen (... 36 chars omitted)
child 0, graph_density: double
child 1, clustering_coefficient: double
child 2, avg_deg
...
_sec: double
child 7, rationale: string
child 8, ensemble_backend: string
child 9, rankings: list<item: struct<solver_id: string, score: double, predicted_runtime_sec: double, predicted_objecti (... 447 chars omitted)
child 0, item: struct<solver_id: string, score: double, predicted_runtime_sec: double, predicted_objective_5s: doub (... 435 chars omitted)
child 0, solver_id: string
child 1, score: double
child 2, predicted_runtime_sec: double
child 3, predicted_objective_5s: double
child 4, predicted_objective_30s: double
child 5, optimality_probability: double
child 6, config: struct<time_limit_sec: int64, num_search_workers: int64, presolve: bool, search_branching: string, h (... 257 chars omitted)
child 0, time_limit_sec: int64
child 1, num_search_workers: int64
child 2, presolve: bool
child 3, search_branching: string
child 4, heuristic_frequency: double
child 5, population_size: int64
child 6, mutation_rate: double
child 7, crossover_rate: double
child 8, solver: string
child 9, neighborhood_size: int64
child 10, destroy_pct: double
child 11, initial_solution: string
child 12, presolving: bool
child 13, separating: bool
child 14, heuristics: bool
child 15, threads: int64
to
{'instance_id': Value('string'), 'size': Value('string'), 'seed': Value('int64'), 'label': Value('string'), 'features': {'fjssp': {'n_jobs': Value('int64'), 'n_machines': Value('int64'), 'n_operations': Value('int64'), 'machine_flexibility': Value('float64'), 'processing_time_variance': Value('float64'), 'workload_skew': Value('float64'), 'precedence_density': Value('float64'), 'alternative_machine_ratio': Value('float64'), 'setup_density': Value('float64')}, 'graph': {'graph_density': Value('float64'), 'clustering_coefficient': Value('float64'), 'avg_degree': Value('float64'), 'connected_components': Value('int64'), 'treewidth_approx': Value('float64')}}, 'feature_vector': List(Value('float64')), 'prediction': {'recommended_solver': Value('string'), 'recommended_config': {'time_limit_sec': Value('int64'), 'num_search_workers': Value('int64'), 'presolve': Value('bool'), 'search_branching': Value('string'), 'heuristic_frequency': Value('float64')}, 'confidence': Value('float64'), 'predicted_objective_5s': Value('float64'), 'predicted_objective_30s': Value('float64'), 'optimality_probability': Value('float64'), 'predicted_runtime_sec': Value('float64'), 'rationale': Value('string'), 'ensemble_backend': Value('string'), 'rankings': List({'solver_id': Value('string'), 'score': Value('float64'), 'predicted_runtime_sec': Value('float64'), 'predicted_objective_5s': Value('float64'), 'predicted_objective_30s': Value('float64'), 'optimality_probability': Value('float64'), 'config': {'time_limit_sec': Value('int64'), 'num_search_workers': Value('int64'), 'presolve': Value('bool'), 'search_branching': Value('string'), 'heuristic_frequency': Value('float64'), 'population_size': Value('int64'), 'mutation_rate': Value('float64'), 'crossover_rate': Value('float64'), 'solver': Value('string'), 'neighborhood_size': Value('int64'), 'destroy_pct': Value('float64'), 'initial_solution': Value('string'), 'presolving': Value('bool'), 'separating': Value('bool'), 'heuristics': Value('bool'), 'threads': Value('int64')}})}}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
MetaSolver-FJSP Instance Features
Structured FJSSP instance features and meta-model predictions for solver selection.
Feature Groups
- FJSSP: jobs, machines, operations, flexibility, variance, skew, precedence, alt-machine ratio, setup density
- Graph: density, clustering, degree, components, treewidth approximation
Reference
Training corpus: zuhdifr/algorithm_selector_fjssp_matrix
License
Apache 2.0
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