Competitive facility location under attrition

Zvi Drezner () and Dawit Zerom ()
Additional contact information
Zvi Drezner: California State University-Fullerton

Computational Management Science, 2023, vol. 20, issue 1, No 39, 19 pages

Abstract: Abstract In this paper we address the possibility that in a competitive facility location model, one of the existing competing facilities will go out of business. We find the best location for a new facility protecting against such a possibility. Four commonly used decision rules (optimistic, pessimistic, minimax regret, and expected value) are analyzed and optimally solved within a given relative accuracy. The results of extensive computational experiments are reported. Special upper bounds, that may be a basis for other optimization problems, are designed. They are much tighter than existing lower bounds. The number if iterations is reduced by a factor close to 5000, and consequently run times were improved by about the same factor. The largest instance of 10 existing competing facilities and 20,000 demand points was solved in less than one second by each of the four decision criteria. The idea of possible scenarios can be investigated by other models in future research.

Keywords: Facility location; Decision analysis; Competitive facility location; Solution algorithms (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10287-023-00473-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:comgts:v:20:y:2023:i:1:d:10.1007_s10287-023-00473-z

Ordering information: This journal article can be ordered from
http://www.springer. ... ch/journal/10287/PS2

DOI: 10.1007/s10287-023-00473-z

Access Statistics for this article

Computational Management Science is currently edited by Ruediger Schultz

More articles in Computational Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().