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..
Risk-Aware Proactive Scheduling via Conditional Value-at-Risk
Authors: Wen Song, Donghun Kang, Jie Zhang, Hui Xi
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results show that our algorithm outperforms state-of-the-art approaches with higher solution quality. We conduct extensive experiments on benchmark instances and commonly used uncertainty models. |
| Researcher Affiliation | Collaboration | Rolls-Royce@NTU Corp Lab, Nanyang Technological University, Singapore a School of Computer Science and Engineering, Nanyang Technological University, Singapore b Rolls-Royce Singapore Pte Ltd, Singapore |
| Pseudocode | Yes | Algorithm 1: Bn B(GV , G H, ω , ˆF β) |
| Open Source Code | No | The paper does not provide any statement about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper generates its own RCPSP instances using 'Ran Gen2 (Vanhoucke et al. 2008)' and models uncertainty using 'a normal distribution N(d0 i , σ2)' and 'an exponential distribution Exp(1/d0 i )'. It does not refer to or provide access to a specific publicly available dataset used for training in the conventional sense. |
| Dataset Splits | No | The paper mentions using 'Q samples independently drawn from y' for SAA and 'Qt = 2000 testing samples' for PoF computation, but it does not specify explicit training/validation/test dataset splits with percentages, sample counts, or references to predefined splits as is common for reproducibility. |
| Hardware Specification | Yes | All algorithms run on an Intel Xeon E5 Workstation (3.5GHz, 16GB). |
| Software Dependencies | Yes | Our algorithm is implemented in JAVA 1.8, while SORU-H and BACCHUS are coded using Java API for CPLEX 12.7.1. |
| Experiment Setup | Yes | α is set to 0.2 in this section. We conduct experiments on the 72 instances used in Section 6.1, with ϵ {0, 0.05, 0.1}. In the remaining experiments, we set Q = 100. |