Publication: A Discrete Competitive Facility Location Model with Minimal Market Share Constraints and Equity-Based Ties Breaking Rule
Authors
Fernández Hernández, Pascual ; Lancinskas, Algirdas ; Pelegrín Pelegrín, Blas ; Zilinskas, Julius
item.page.secondaryauthor
item.page.director
Publisher
IOS PRESS
publication.page.editor
publication.page.department
DOI
https://doi.org/10.15388/20-INFOR410
item.page.type
info:eu-repo/semantics/article
Description
©2020. This manuscript version is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by /4.0/
This document is the Published, version of a Published Work that appeared in final form in Informatica: An International Journal. To access the final edited and published work see https://doi.org/10.15388/20-INFOR410
Abstract
We consider a geographical region with spatially separated customers, whose demand is currently served by some pre-existing facilities owned by different firms. An entering firm wants to compete for this market locating some new facilities. Trying to guarantee a future satisfactory captured demand for each new facility, the firm imposes a constraint over its possible locations (a finite set of candidates): a new facility will be opened only if a minimal market share is captured in the short-term. To check that, it is necessary to know the exact captured demand by each new facility. It is supposed that customers follow the partially binary choice rule to satisfy its demand. If there are several new facilities with maximal attraction for a customer, we consider that the proportion of demand captured by the entering firm will be equally distributed among such facilities (equity-based rule). This ties breaking rule involves that we will deal with a nonlinear constrained discrete competitive facility location problem. Moreover, minimal attraction conditions for customers and distances approximated by intervals have been incorporated to deal with a more realistic model. To solve this nonlinear model, we first linearize the model, which allows to solve small size problems because of its complexity, and then, for bigger size problems, a heuristic algorithm is proposed, which could also be used to solve other constrained problems.
publication.page.subject
Citation
Informatica: An International Journal (2020) 205–224
item.page.embargo
Collections
Ir a Estadísticas
Este ítem está sujeto a una licencia Creative Commons. http://creativecommons.org/licenses/by/4.0/