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---
license: mit
language:
- en
tags:
- optimization
- planning
pretty_name: L-RIPLIB
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: Easy
path: "data/Easy/*.json"
- split: Normal
path: "data/Normal/*.json"
- split: Hard
path: "data/Hard/*.json"
---
# 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 between `q` and `q'` (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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