Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
NICE: Robust Scheduling through Reinforcement Learning-Guided Integer Programming
Authors: Luke Kenworthy, Siddharth Nayak, Christopher Chin, Hamsa Balakrishnan9821-9829
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that, across a variety of scenarios, NICE produces schedules resulting in 33% to 48% fewer disruptions than the baseline formulation. |
| Researcher Affiliation | Collaboration | Luke Kenworthy1, Siddharth Nayak2, Christopher Chin2, Hamsa Balakrishnan2 1 US Air Force-MIT AI Accelerator 2 MIT EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methods in prose but does not contain a formal pseudocode or algorithm block. |
| Open Source Code | Yes | 1Our code is available at https://github.com/nsidn98/NICE |
| Open Datasets | No | We utilized an anonymized dataset from a flying squadron to construct a random event generator. The dataset contained 87 pilots with 32 different qualifications and 801 flights across over six months, each containing between 2 3 slots. The paper mentions it's an 'anonymized dataset' from a 'flying squadron' and provides no public access information (link, DOI, specific citation to a public repository). |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits with specific percentages or counts. |
| Hardware Specification | No | The paper acknowledges the use of 'MIT Super Cloud' and 'Lincoln Laboratory Supercomputing Center' for 'high performance computing resources' but does not provide specific hardware details like GPU or CPU models. |
| Software Dependencies | No | The paper mentions using 'Open AI Gym' and 'Proximal Policy Optimization (PPO)' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | The hyperparameters for all our experiments are listed in the Appendix (Kenworthy et al. 2021). |