The generalized maximal covering location problem
Computers and Operations Research - Location analysis
Parallel machine scheduling models with fuzzy processing times
Information Sciences—Informatics and Computer Science: An International Journal
Solving the maximal covering location problem with heuristic concentration
Computers and Operations Research
Discrete models for competitive location with foresight
Computers and Operations Research
Fuzzy logic based algorithms for maximum covering location problems
Information Sciences: an International Journal
Flexible job-shop scheduling with parallel variable neighborhood search algorithm
Expert Systems with Applications: An International Journal
The Ordered Gradual Covering Location Problem on a Network
Discrete Applied Mathematics
Standby redundancy optimization problems with fuzzy lifetimes
Computers and Industrial Engineering
On the improvements of the particle swarm optimization algorithm
Advances in Engineering Software
Review: Generalized coverage: New developments in covering location models
Computers and Operations Research
The multi-depot capacitated location-routing problem with fuzzy travel times
Expert Systems with Applications: An International Journal
Expected value of fuzzy variable and fuzzy expected value models
IEEE Transactions on Fuzzy Systems
Maximal covering location problem (MCLP) with fuzzy travel times
Expert Systems with Applications: An International Journal
Capacitated location-routing problem with time windows under uncertainty
Knowledge-Based Systems
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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.