# Reallocating Multiple Facilities on the Line

@article{Fotakis2019ReallocatingMF, title={Reallocating Multiple Facilities on the Line}, author={Dimitris Fotakis and Loukas Kavouras and Panagiotis Kostopanagiotis and Philip Lazos and Stratis Skoulakis and Nikolas Zarifis}, journal={ArXiv}, year={2019}, volume={abs/1905.12379} }

We study the multistage K-facility reallocation problem on the real line, where we maintain K facility locations over T stages, based on the stage-dependent locations of n agents. Each agent is connected to the nearest facility at each stage, and the facilities may move from one stage to another, to accommodate different agent locations. The objective is to minimize the connection cost of the agents plus the total moving cost of the facilities, over all stages. K-facility reallocation problem…

## 4 Citations

Facility Reallocation on the Line

- EconomicsIJCAI
- 2018

This work considers a multi-stage facility reallocation problems on the real line, where a facility is being moved between stages based on the locations reported by n agents, and proposes another strategyproof mechanism which has a competitive ratio of (n+3)/(n+1) for odd n and ( n+4)/n for even n, which it conjecture to be the best possible.

Mobile resource allocation

- Computer Science
- 2020

This thesis covers the topic of resource allocation problems that are tailored to scenarios primarily involving mobile users, and proposes online algorithms that achieve competitive ratios independent of time and the number of clients.

Mechanism Design for Facility Location Problems: A Survey

- EconomicsIJCAI
- 2021

A comprehensive survey of the significant progress that has been made since the introduction of the approximate mechanism design problem is presented, highlighting the different variants and methodologies, as well as the most interesting directions for future research.

Efficient Online Learning for Dynamic k-Clustering

- Computer ScienceICML
- 2021

An online learning problem, called Dynamic kClustering, in which k centers are maintained in a metric space over time (centers may change positions) such as a dynamically changing set of r clients is served in the best possible way and this work contributes to the long line of research on combinatorial online learning.

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This work considers a multi-stage facility reallocation problems on the real line, where a facility is being moved between stages based on the locations reported by n agents, and proposes another strategyproof mechanism which has a competitive ratio of (n+3)/(n+1) for odd n and ( n+4)/n for even n, which it conjecture to be the best possible.

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