An approach for solving maximal covering location problems with fuzzy constraints

DOI

10.1080/18756891.2016.1204121How to use a DOI?

Keywords

Location Problems; Maximal Covering; Uncertainty; Soft Computing; Fuzzy sets

Abstract

Several real-world situations can be modeled as maximal covering location problem (MCLP), which is focused on finding the best locations for a certain number of facilities that maximizes the coverage of demand nodes located within a given exact coverage distance (or travel time). In a real scenario, such distance as well as other elements of the location problem can be uncertain or linguistically (vaguely) defined by the decision maker. In this paper, we manage flexibility in the coverage distance through a fuzzy constraint. So, an extension of the original MCLP model is proposed, which is solved by using a parametric approach. Computational experiments have been conducted to analyze how the proposed model can be solved and what kind of information can be obtained to help a potential decision maker to take a more informed decision.

Copyright

© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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TY  - JOUR
AU  - Virgilio C. Guzmán
AU  - David A. Pelta
AU  - José L. Verdegay
PY  - 2016
DA  - 2016/08/01
TI  - An approach for solving maximal covering location problems with fuzzy constraints
JO  - International Journal of Computational Intelligence Systems
SP  - 734
EP  - 744
VL  - 9
IS  - 4
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2016.1204121
DO  - 10.1080/18756891.2016.1204121
ID  - Guzmán2016
ER  -