iScheduler: Reinforcement Learning-Driven Continual Optimization for Large-Scale Resource Investment Problems
Paper • 2602.06064 • Published
Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
return check_status(status)
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to array in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 101, in _split_generators
pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 304, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 66, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
L-RIPLIB
Dataset Summary
L-RIPLIB is an industrial-scale benchmark for Resource Investment Problems (RIP) derived from cloud computing workloads. It contains 1,000 instances with problem sizes ranging from 2,500 to 10,000 tasks, intended to support realistic large-scale evaluation and to complement smaller classical benchmarks (e.g., PSPLIB).
Each instance is stored as a JSON record describing a task set with time windows, durations, precedence constraints, per-task resource requirements, and solution-related metadata produced by OR-Tools CP-SAT under a time cap.
Supported Tasks and Usage Scenarios
This dataset is suitable for:
- Large-scale project/task scheduling with precedence constraints and time windows.
- Resource provisioning / resource investment with per-resource unit costs.
- Learning-augmented optimization (e.g., predicting good schedules, costs, bounds, or warm-start solutions).
- Dynamic / continual re-optimization experiments using the provided “modified_data” deltas (see “Modified_data” field).
Languages
- English
Dataset Structure
Data Format
- One JSON object per instance.
Data Fields (per instance)
The dataset uses the following key elements:
- Tasks (
T): list of task names (activities) within the instance. - Earliest_start (
e): earliest start time for each task. - Deadline (
l): deadline / latest finish time for each task. - Duration (
d): duration for each task. - Dependencies (
P): precedence constraints specifying which tasks must finish before others can start. - Resources (
R): resources allocated to each task (resource requirements). - Costs (
c): unit cost of each resource type. - Task_start (
(S_i)_{i∈T}): a CP-SAT solution (task start times) obtained under a limited time budget of 0.1 × |T| seconds. - Best_cost: total resource cost for the provided solution.
- Time: CP-SAT solve time for the instance.
- Bound: CP-SAT lower bound on total resource cost.
- Modified_data (
Δq): the difference betweenqandq'(used to represent instance modifications).
Citation
If you find our work helpful, feel free to give us a cite.
@misc{hu2026ischedulerreinforcementlearningdrivencontinual,
title={iScheduler: Reinforcement Learning-Driven Continual Optimization for Large-Scale Resource Investment Problems},
author={Yi-Xiang Hu and Yuke Wang and Feng Wu and Zirui Huang and Shuli Zeng and Xiang-Yang Li},
year={2026},
eprint={2602.06064},
archivePrefix={arXiv},
primaryClass={cs.DC},
url={https://arxiv.org/abs/2602.06064},
}
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