Risk-Aware Proactive Scheduling via Conditional Value-at-Risk
Authors: Wen Song, Donghun Kang, Jie Zhang, Hui Xi
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | 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. |