| Title |
A discrete competitive facility location model with minimal market share constraints and equity-based ties breaking rule / |
| Authors |
Fernandez, Pascual ; Lančinskas, Algirdas ; Pelegrin, Blas ; Žilinskas, Julius |
| DOI |
10.15388/20-INFOR410 |
| Full Text |
|
| Is Part of |
Informatica.. Vilnius : Vilnius University Institute of Data Science and Digital Technologies. 2020, vol. 31, iss. 2, p. 205-224.. ISSN 0868-4952. eISSN 1822-8844 |
| Keywords [eng] |
Location ; Competitive model ; Heuristic algorithms |
| Abstract [eng] |
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. |
| Published |
Vilnius : Vilnius University Institute of Data Science and Digital Technologies |
| Type |
Journal article |
| Language |
English |
| Publication date |
2020 |
| CC license |
|
