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Multilayer variable neighborhood search for two‐level uncapacitated facility location problems with single assignment
Author(s) -
Gendron Bernard,
Khuong PaulVirak,
Semet Frédéric
Publication year - 2015
Publication title -
networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.977
H-Index - 64
eISSN - 1097-0037
pISSN - 0028-3045
DOI - 10.1002/net.21626
Subject(s) - variable neighborhood search , mathematical optimization , solver , heuristic , metaheuristic , modular design , integer programming , variable (mathematics) , set (abstract data type) , class (philosophy) , facility location problem , computer science , integer (computer science) , mathematics , local search (optimization) , artificial intelligence , mathematical analysis , programming language , operating system
We develop a variant of the variable neighborhood search (VNS) metaheuristic called the multilayer VNS (MLVNS). It consists in partitioning the neighborhood structures into multiple layers. For each layer l , a VNS defined on the associated neighborhood structures is invoked, each move being evaluated and completed by a recursive call to the MLVNS at layer l − 1 . A specific MLVNS is developed to solve approximately a class of two‐level uncapacitated facility location problems with single assignment (TUFLPS), when only mild assumptions are imposed on the cost functions. Two special cases are used to illustrate the efficiency of the MLVNS: the classical TUFLPS and a problem with modular costs derived from a real‐life case. To assess the efficiency of the MLVNS, computational results on a large set of instances are compared with those obtained by slope scaling heuristic methods and by solving integer programming models using a state‐of‐the‐art commercial solver. © 2015 Wiley Periodicals, Inc. NETWORKS, Vol. 66(3), 214–234 2015