Efficient and Equitable Deployment of Mobile Vaccine Distribution Centers
Authors: Da Qi Chen, Ann Li, George Z. Li, Madhav Marathe, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then run extensive simulations on real world datasets to show the efficacy of our methods. |
| Researcher Affiliation | Academia | Da Qi Chen1, Ann Li1, George Z. Li2, Madhav Marathe1, Aravind Srinivasan2, Leonidas Tsepenekas2, and Anil Vullikanti1 1Biocomplexity Institute & Initiative, University of Virginia 2University of Maryland |
| Pseudocode | Yes | Algorithm 1 COVER and Algorithm 2 Network-Flow Algorithm |
| Open Source Code | No | The paper does not contain any statements about releasing open-source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | We run experiments on synthetic mobility data for a city and county in Virginia. The dataset was constructed from the 2019 U.S. population pipeline (see [Chen et al., 2022; Machi et al., 2021] for details). The paper describes how the synthetic data was constructed but does not provide a direct link, DOI, or specific repository name for the dataset used in its experiments. |
| Dataset Splits | No | The paper describes the generation of 'demand input' and 'randomly generated instances' for the experiments, but it does not specify explicit training, validation, or test dataset splits (e.g., percentages or counts). |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models, processor types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers (e.g., Python 3.x, PyTorch x.x, CPLEX x.x) that would be needed to replicate the experiments. |
| Experiment Setup | Yes | Experimental Setup: We run experiments on synthetic mobility data for a city and county in Virginia. ... We set all non-residential locations within each municipality as potential facility locations. ... we extract a day of the week and examine the hours of 6am-8pm for a total of T = 14 hour-long timesteps. ... We conduct all our experiments on 10 randomly generated instances of the demand input... We set the movement constraint to M = 5km... This experiment varies the budget k from 3 10 in Charlottesville and 6 20 for Albermarle... |