Online Task Assignment Problems with Reusable Resources
Authors: Hanna Sumita, Shinji Ito, Kei Takemura, Daisuke Hatano, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi5199-5207
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments We evaluate the performance of our algorithm through experiments. We use a synthetic dataset and the real-world dataset of taxi trip records, similarly to previous work (Dickerson et al. 2021; Nanda et al. 2020). |
| Researcher Affiliation | Collaboration | 1 Tokyo Institute of Technology 2 NEC Corporation 3 RIKEN AIP 4 Chuo University 5 Keio University 6 National Institute of Informatics |
| Pseudocode | Yes | Algorithm 1: Proposed online algorithm |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing the code or a direct link to a code repository. It mentions 'see the full version (Sumita et al. 2022)' for omitted details, which is not an explicit code release. |
| Open Datasets | Yes | We evaluate the performance for instances generated from the New York City yellow cabs dataset5, which is used also in existing work such as (Dickerson et al. 2021; Nanda et al. 2020). 5http://www.andresmh.com/nyctaxitrips/ |
| Dataset Splits | No | The paper does not specify exact dataset split percentages or sample counts for training, validation, or test sets for its experiments. It mentions using 'synthetic dataset' and 'real-world dataset' but no split details. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, or memory specifications, used for running the experiments. It only mentions runtime metrics. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., programming language versions, library versions, or specific LP solver names and versions). |
| Experiment Setup | Yes | Instance Setting We focus on the following four settings (a) (d)... We set |U| = 30, |V | = 100, and T = 200. For each u U and v V , an edge (u, v) exists in E with probability 0.1. For each e E, we set qe U(0.5, 1) (uniform distribution) and we U(0, 1), respectively. ... We set the same bv for all v from {2, 4, 6, 8, 10}. |