Facility’s Perspective to Fair Facility Location Problems

Authors: Chenhao Wang, Xiaoying Wu, Minming Li, Hau Chan5734-5741

AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We assess the availability, existence, approximability, and the quality (price of fairness) of fair solutions, where the quality measures the system efficiency loss under a fair allocation compared to the one that maximizes the social welfare. further, we show that one can find a Pareto-optimal solution in polynomial time. In Section 3, we first prove that the problem of deter-mining the existence of a Prop allocation is NP-complete, using a similar reduction in (Bouveret et al. 2017) for the FDC. We show that for any instance, there exists a valid n+m 1 2m umax-Prop allocation where umax is highest value of any facility for any item. On the other hand, the existence of a valid ( n 12 umax ϵ)-Prop allocation is not guaranteed for any ϵ > 0, even if there are m = 3 facilities.
Researcher Affiliation Academia Chenhao Wang,1 Xiaoying Wu,2,3 Minming Li,4 Hau Chan1 1 University of Nebraska-Lincoln 2 AMSS, Chinese Academy of Sciences 3 University of Chinese Academy of Sciences 4 City University of Hong Kong chenhwang4-c@my.cityu.edu.hk, xywu@amss.ac.cn, minming.li@cityu.edu.hk, hchan3@unl.edu
Pseudocode Yes Algorithm 1 Pareto-optimal valid allocation
Open Source Code No The paper does not provide concrete access to source code for the methodology described.
Open Datasets No The paper is theoretical and does not conduct experiments on a dataset.
Dataset Splits No The paper is theoretical and does not use dataset splits for validation.
Hardware Specification No The paper is theoretical and does not describe hardware specifications for experiments.
Software Dependencies No The paper is theoretical and does not mention specific software dependencies with version numbers.
Experiment Setup No The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations.