Competitive Analysis for Two-Level Ski-Rental Problem
Authors: Binghan Wu, Wei Bao, Dong Yuan12034-12041
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | 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 biwu6051@uni.sydney.edu.au, wei.bao@sydney.edu.au, dong.yuan@sydney.edu.au |
| 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]. |