Document Open Access Logo

A Tree Structure For Dynamic Facility Location

Authors Gramoz Goranci, Monika Henzinger, Dariusz Leniowski

PDF
Thumbnail PDF

File

LIPIcs.ESA.2018.39.pdf
  • Filesize: 477 kB
  • 13 pages

Document Identifiers

Author Details

Gramoz Goranci
  • University of Vienna, Faculty of Computer Science, Vienna, Austria
Monika Henzinger
  • University of Vienna, Faculty of Computer Science, Vienna, Austria
Dariusz Leniowski
  • University of Vienna, Faculty of Computer Science, Vienna, Austria

Funding

The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement no. 340506.

Cite AsGet BibTex

Gramoz Goranci, Monika Henzinger, and Dariusz Leniowski. A Tree Structure For Dynamic Facility Location. In 26th Annual European Symposium on Algorithms (ESA 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 112, pp. 39:1-39:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/LIPIcs.ESA.2018.39

Abstract

We study the metric facility location problem with client insertions and deletions. This setting differs from the classic dynamic facility location problem, where the set of clients remains the same, but the metric space can change over time. We show a deterministic algorithm that maintains a constant factor approximation to the optimal solution in worst-case time O~(2^{O(kappa^2)}) per client insertion or deletion in metric spaces while answering queries about the cost in O(1) time, where kappa denotes the doubling dimension of the metric. For metric spaces with bounded doubling dimension, the update time is polylogarithmic in the parameters of the problem.

Subject Classification

ACM Subject Classification
  • Theory of computation → Facility location and clustering
Keywords
  • facility location
  • dynamic algorithm
  • approximation
  • doubling dimension

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

References

  1. Sebastian Abshoff, Peter Kling, Christine Markarian, Friedhelm Meyer auf der Heide, and Peter Pietrzyk. Towards the price of leasing online. Journal of Combinatorial Optimization, 4(32):1197-1216, 2015. Google Scholar
  2. Hyung-Chan An, Ashkan Norouzi-Fard, and Ola Svensson. Dynamic facility location via exponential clocks. In Symposium on Discrete Algorithms (SODA), pages 708-721, 2015. Google Scholar
  3. Aris Anagnostopoulos, Russell Bent, Eli Upfal, and Pascal Van Hentenryck. A simple and deterministic competitive algorithm for online facility location. Information and Computation, 194(2):175-202, 2004. Google Scholar
  4. Barbara Anthony and Anupam Gupta. Infrastructure leasing problems. International Conference on Integer Programming and Combinatorial Optimization, pages 424-438, 2007. Google Scholar
  5. Pierre Chardaire, Alain Sutter, and Marie-Christine Costa. Solving the dynamic facility location problem. Networks, 28(2):117-124, 1996. Google Scholar
  6. Artur Czumaj, Christiane Lammersen, Morteza Monemizadeh, and Christian Sohler. (1+ε)-approximation for facility location in data streams. In Symposium on Discrete Algorithms (SODA), pages 1710-1728, 2013. Google Scholar
  7. Zvi Drezner and Horst W Hamacher. Facility location: applications and theory. Springer Science &Business Media, 2001. Google Scholar
  8. David Eisenstat, Claire Mathieu, and Nicolas Schabanel. Facility location in evolving metrics. In International Colloquium on Automata, Languages, and Programming (ICALP), pages 459-470, 2014. Google Scholar
  9. Reza Zanjirani Farahani, Maryam Abedian, and Sara Sharahi. Dynamic facility location problem. In Facility Location, pages 347-372. Springer, 2009. Google Scholar
  10. Dimitris Fotakis. A primal-dual algorithm for online non-uniform facility location. Journal of Discrete Algorithms, 5(1):141-148, 2007. Google Scholar
  11. Dimitris Fotakis. On the competitive ratio for online facility location. Algorithmica, 50(1):1-57, 2008. Google Scholar
  12. Dimitris Fotakis. Memoryless facility location in one pass. ACM Trans. Algorithms, 7(4):49:1-49:24, 2011. Google Scholar
  13. Sudipto Guha and Samir Khuller. Greedy strikes back: Improved facility location algorithms. Journal of Algorithms, 31(1):228-248, 1999. Google Scholar
  14. Monika Henzinger, Dariusz Leniowski, and Claire Mathieu. Dynamic clustering to minimize the sum of radii. In European Symposium on Algorithms (ESA), 2017. Google Scholar
  15. Piotr Indyk. Algorithms for dynamic geometric problems over data streams. In Symposium on Theory of Computing (STOC), pages 373-380, 2004. Google Scholar
  16. Christiane Lammersen and Christian Sohler. Facility location in dynamic geometric data streams. In European Symposium on Algorithms (ESA), pages 660-671, 2008. Google Scholar
  17. Shi Li. A 1.488 approximation algorithm for the uncapacitated facility location problem. Information and Computation, 222:45-58, 2013. Google Scholar
  18. Adam Meyerson. Online facility location. In Symposim on Foundations of Computer Science (FOCS), pages 426-431, 2001. Google Scholar
  19. Chandrashekhar Nagarajan and David P Williamson. Offline and online facility leasing. Discrete Optimization, 10(4):361-370, 2013. Google Scholar
  20. Maxim Sviridenko. An improved approximation algorithm for the metric uncapacitated facility location problem. In International Conference on Integer Programming and Combinatorial Optimization (IPCO), pages 240-257, 2002. Google Scholar
  21. George O Wesolowsky. Dynamic facility location. Management Science, 19(11):1241-1248, 1973. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH Imprint Privacy Contact