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..
Competitive Analysis for Two-Level Ski-Rental Problem
Authors: Binghan Wu, Wei Bao, Dong Yuan12034-12041
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we apply simulation to verify the theoretical competitive ratios and evaluate the actual performance against benchmarks. |
| Researcher Affiliation | Academia | Binghan Wu, Wei Bao, Dong Yuan Faculty of Engineering, The University of Sydney EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Deterministic Two-level Ski-Rental Algorithm DTSR(λs, λc) |
| Open Source Code | No | The paper does not provide explicit links to open-source code for the described methodology or state that the code is available. |
| Open Datasets | No | The input sequence is generated randomly in this simulation. We set the number of instances as 6. For each single sequence, we generate S tasks. Each task has a unit demand. S is uniformly distributed from 1 to 60. The inter-arrival time of tasks is uniformly distributed in [0, 1]. |
| Dataset Splits | No | The paper describes simulation settings and sequence generation but does not specify training, validation, or test dataset splits. |
| Hardware Specification | Yes | We use Python 3.6.8 with numpy 1.18.1 to conduct the simulation in a laptop with CPU i5-8210Y and Memory 8 GB 2133 MHz LPDDR3. |
| Software Dependencies | Yes | We use Python 3.6.8 with numpy 1.18.1 to conduct the simulation in a laptop with CPU i5-8210Y and Memory 8 GB 2133 MHz LPDDR3. |
| Experiment Setup | Yes | We set the number of instances as 6. For each single sequence, we generate S tasks. Each task has a unit demand. S is uniformly distributed from 1 to 60. The inter-arrival time of tasks is uniformly distributed in [0, 1]. |