When closest is not always the best: The distributed p-median problem

Jack Brimberg, Andrea Maier and Anita Schöbel

Journal of the Operational Research Society, 2021, vol. 72, issue 1, 200-216

Abstract: The classical p-median problem assumes that service to customers is always provided by the closest facility, while in practice, customers often interact for a variety of reasons with several of the facilities (not just the closest). In this article, we examine the concept of a distribution rule for modelling a more general case where the demand of a customer is not entirely satisfied by its closest facility, but rather is split into different flows to different facilities according to the given rule. We use this concept to formulate a new class of median problems, which we call the “distributed” p-median problem. Different types of distribution rules are investigated leading to some interesting properties. For example, if the weights are increasing (ie, assigned flows are greater to facilities that are further away), the problem can be solved in polynomial time as a 1-median problem. For decreasing weights, we obtain new and efficient generalizations of the standard continuous and discrete p-median models, which in turn lead to a broader interpretation of median points and a generalization of Cooper’s well-known locate–allocate heuristic. Some small numerical examples and computational results are given to illustrate the concepts.

Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2019.1654940 (text/html)
Access to full text is restricted to subscribers.

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:taf:tjorxx:v:72:y:2021:i:1:p:200-216

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20

DOI: 10.1080/01605682.2019.1654940

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald

More articles in Journal of the Operational Research Society from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().