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--- |
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license: mit |
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language: |
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- en |
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tags: |
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- optimization |
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- planning |
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pretty_name: L-RIPLIB |
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size_categories: |
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- 1K<n<10K |
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configs: |
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- config_name: default |
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data_files: |
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- split: Easy |
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path: "data/Easy/*.json" |
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- split: Normal |
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path: "data/Normal/*.json" |
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- split: Hard |
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path: "data/Hard/*.json" |
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--- |
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# L-RIPLIB |
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## Dataset Summary |
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**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). |
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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. |
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## Supported Tasks and Usage Scenarios |
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This dataset is suitable for: |
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- **Large-scale project/task scheduling** with precedence constraints and time windows. |
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- **Resource provisioning / resource investment** with per-resource unit costs. |
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- **Learning-augmented optimization** (e.g., predicting good schedules, costs, bounds, or warm-start solutions). |
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- **Dynamic / continual re-optimization** experiments using the provided “modified_data” deltas (see “Modified_data” field). |
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## Languages |
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- English |
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## Dataset Structure |
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### Data Format |
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- **One JSON object per instance**. |
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### Data Fields (per instance) |
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The dataset uses the following key elements: |
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- **Tasks (`T`)**: list of task names (activities) within the instance. |
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- **Earliest_start (`e`)**: earliest start time for each task. |
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- **Deadline (`l`)**: deadline / latest finish time for each task. |
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- **Duration (`d`)**: duration for each task. |
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- **Dependencies (`P`)**: precedence constraints specifying which tasks must finish before others can start. |
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- **Resources (`R`)**: resources allocated to each task (resource requirements). |
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- **Costs (`c`)**: unit cost of each resource type. |
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- **Task_start (`(S_i)_{i∈T}`)**: a CP-SAT solution (task start times) obtained under a limited time budget of **0.1 × |T| seconds**. |
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- **Best_cost**: total resource cost for the provided solution. |
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- **Time**: CP-SAT solve time for the instance. |
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- **Bound**: CP-SAT lower bound on total resource cost. |
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- **Modified_data (`Δq`)**: the difference between `q` and `q'` (used to represent instance modifications). |
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## Citation |
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If you find our work helpful, feel free to give us a cite. |
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``` |
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@misc{hu2026ischedulerreinforcementlearningdrivencontinual, |
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title={iScheduler: Reinforcement Learning-Driven Continual Optimization for Large-Scale Resource Investment Problems}, |
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author={Yi-Xiang Hu and Yuke Wang and Feng Wu and Zirui Huang and Shuli Zeng and Xiang-Yang Li}, |
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year={2026}, |
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eprint={2602.06064}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.DC}, |
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url={https://arxiv.org/abs/2602.06064}, |
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} |
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``` |
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