A greedy variable neighborhood search heuristic for the maximal covering location problem with fuzzy coverage radii

Soheil Davari; Mohammad Hossein Fazel Zarandi; Burhan Turksen

doi:10.1016/j.knosys.2012.12.012

A greedy variable neighborhood search heuristic for the maximal covering location problem with fuzzy coverage radii

Soheil Davari, Mohammad Hossein Fazel Zarandi, Burhan Turksen

Research output: Contribution to journal › Article › peer-review

40 Citations (SciVal)

Abstract

The maximal covering location problem (MCLP) seeks location of facilities on a network, so as to maximize the total demand within a pre-defined distance or travel time of facilities (which is called coverage radius). Most of the real-world applications of MCLP comprise many demand nodes to be covered. Moreover, uncertainty is ubiquitous in most of the real-world covering location problems, which are solved for a long-term horizon. Therefore, this paper studies a large-scale MCLP on the plane with fuzzy coverage radii under the Hurwicz criterion. In order to solve the problem, a combination of variable neighborhood search (VNS) and fuzzy simulation is offered. Test problems with up to 2500 nodes and different settings show that VNS is competitive, since it is able to find solutions with gaps all below 1.5% in much less time compared to exact algorithms.

Original language	English
Pages (from-to)	68-76
Number of pages	9
Journal	Knowledge-Based Systems
Volume	41
DOIs	https://doi.org/10.1016/j.knosys.2012.12.012
Publication status	Published - 2013

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cited By 27

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title = "A greedy variable neighborhood search heuristic for the maximal covering location problem with fuzzy coverage radii",

abstract = "The maximal covering location problem (MCLP) seeks location of facilities on a network, so as to maximize the total demand within a pre-defined distance or travel time of facilities (which is called coverage radius). Most of the real-world applications of MCLP comprise many demand nodes to be covered. Moreover, uncertainty is ubiquitous in most of the real-world covering location problems, which are solved for a long-term horizon. Therefore, this paper studies a large-scale MCLP on the plane with fuzzy coverage radii under the Hurwicz criterion. In order to solve the problem, a combination of variable neighborhood search (VNS) and fuzzy simulation is offered. Test problems with up to 2500 nodes and different settings show that VNS is competitive, since it is able to find solutions with gaps all below 1.5% in much less time compared to exact algorithms. ",

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AU - Turksen, Burhan

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AB - The maximal covering location problem (MCLP) seeks location of facilities on a network, so as to maximize the total demand within a pre-defined distance or travel time of facilities (which is called coverage radius). Most of the real-world applications of MCLP comprise many demand nodes to be covered. Moreover, uncertainty is ubiquitous in most of the real-world covering location problems, which are solved for a long-term horizon. Therefore, this paper studies a large-scale MCLP on the plane with fuzzy coverage radii under the Hurwicz criterion. In order to solve the problem, a combination of variable neighborhood search (VNS) and fuzzy simulation is offered. Test problems with up to 2500 nodes and different settings show that VNS is competitive, since it is able to find solutions with gaps all below 1.5% in much less time compared to exact algorithms.

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JO - Knowledge-Based Systems

JF - Knowledge-Based Systems

ER -

A greedy variable neighborhood search heuristic for the maximal covering location problem with fuzzy coverage radii

Abstract

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