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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Equitable Scheduling on a Single Machine
Authors: Klaus Heeger, Dan Hermelin, George B. Mertzios, Hendrik Molter, Rolf Niedermeier, Dvir Shabtay11818-11825
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide a thorough analysis of the computational complexity of three main variants of this problem, identifying both ef๏ฌcient algorithms and worst-case intractability results. |
| Researcher Affiliation | Academia | 1 TU Berlin, Faculty IV, Algorithmics and Computational Complexity, Germany 2 Ben Gurion University of the Negev, Beersheba, Israel 3 Department of Computer Science, Durham University, UK |
| Pseudocode | No | The paper describes algorithms in prose and mathematical formulations, but it does not contain structured pseudocode or algorithm blocks with formal labeling like 'Algorithm 1' or 'Pseudocode'. |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that source code for the described methodology is publicly available. It references "Heeger et al. 2020. Equitable Scheduling on a Single Machine. Co RR abs/2010.04643. URL https://arxiv.org/abs/2010.04643" which points to an arXiv preprint, not source code. |
| Open Datasets | No | The paper is theoretical and does not involve training models on datasets. Therefore, no information about publicly available datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on datasets, thus no training/test/validation splits are discussed. |
| Hardware Specification | No | The paper is purely theoretical, focusing on computational complexity and algorithms. It does not describe any experiments that would require specific hardware, and therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithm design and complexity analysis. It does not mention any specific software dependencies with version numbers for reproducing experiments. |
| Experiment Setup | No | The paper is theoretical and does not involve experimental setups, hyperparameters, or system-level training settings. Therefore, no such details are provided. |