Empirical Competitive Analysis of Online Algorithms With Advice for the Online Facility Location Problem

DOI

10.2991/978-94-6239-638-8_22How to use a DOI?

Keywords

Facility location problem; online algorithms; empirical analysis

Abstract

In this study, the Online Facility Location Problem is examined using historical data on 911 EMS calls in Montgomery County. K-means clustering and the offline solution to a past year’s data are both explored as solutions to the online facility location problem and compared to existing online solutions both with and without advice, and an approximation of the offline solution. In the given data set, the proposed solutions were found to outperform the existing solutions, with the most optimistic solution having a competitive ratio of 1.005036. The proposed solutions also show better performance in terms of computation time and memory costs.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jayson Isaiah Tan
AU  - Luis Miguel Senatin
AU  - Jhoirene B. Clemente
PY  - 2026
DA  - 2026/04/30
TI  - Empirical Competitive Analysis of Online Algorithms With Advice for the Online Facility Location Problem
BT  - Proceedings of the  Workshop on Computation: Theory and Practice (WCTP 2025)
PB  - Atlantis Press
SP  - 445
EP  - 465
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6239-638-8_22
DO  - 10.2991/978-94-6239-638-8_22
ID  - Tan2026
ER  -