@Article{PereiraCoelLoreSouz:2015:HyMePr,
author = "Pereira, Marcos A. and Coelho, Leandro C. and Lorena, Luiz Antonio
Nogueira and Souza, Ligia C. de",
affiliation = "{Universidade Estadual Paulista (UNESP)} and Interuniversity
Research Centre on Enterprise Network, Logistics and
Transportation (CIRRELT) and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais
(INPE)}",
title = "A hybrid method for the probabilistic maximal covering location
allocation problem",
journal = "Computers and Operations Research",
year = "2015",
volume = "57",
pages = "51--59",
month = "May",
keywords = "adaptive large neighborhood search, congested systems, exact
method, facility location, hybrid algorithm, PMCLAP, queueing
maximal covering location-allocation model.",
abstract = "This paper presents a hybrid algorithm that combines a
metaheuristic and an exact method to solve the Probabilistic
Maximal Covering Location-Allocation Problem. A linear programming
formulation for the problem presents variables that can be
partitioned into location and allocation decisions. This model is
solved to optimality for small- and medium-size instances. To
tackle larger instances, a flexible adaptive large neighborhood
search heuristic was developed to obtain location solutions,
whereas the allocation subproblems are solved to optimality. An
improvement procedure based on an integer programming method is
also applied. Extensive computational experiments on benchmark
instances from the literature confirm the efficiency of the
proposed method. The exact approach found new best solutions for
19 instances, proving the optimality for 18 of them. The hybrid
method performed consistently, finding the best known solutions
for 94.5% of the instances and 17 new best solutions (15 of them
optimal) for a larger dataset in one-third of the time of a
state-of-the-art solver.",
doi = "10.1016/j.cor.2014.12.001",
url = "http://dx.doi.org/10.1016/j.cor.2014.12.001",
issn = "0305-0548",
language = "en",
urlaccessdate = "27 abr. 2024"
}