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A 2‐stage biased‐randomized iterated local search for the uncapacitated single allocation p ‐hub median problem
Author(s) -
Alvarez Fernandez Stephanie,
Ferone Daniele,
Juan Angel A.,
Silva Daniel G.,
Armas Jesica
Publication year - 2018
Publication title -
transactions on emerging telecommunications technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.366
H-Index - 47
ISSN - 2161-3915
DOI - 10.1002/ett.3418
Subject(s) - iterated local search , iterated function , mathematical optimization , metaheuristic , computer science , spoke hub distribution paradigm , node (physics) , set (abstract data type) , network planning and design , transshipment (information security) , facility location problem , local search (optimization) , randomized algorithm , flow network , mathematics , operations research , algorithm , computer network , engineering , mathematical analysis , structural engineering , transport engineering , programming language
The hub location problem has gained attention over the last decades. In telecommunications network, a hub is a place of concurrence in where the work of the network is centralized with the purpose of delivering out the data that arrives from one or more directions to other destinations. There are different versions of the hub location problem depending upon (1) the existence or not of restrictions on the capacity related to the volume of flow a hub is allowed to support, (2) the existence or not of a set‐up cost associated with selecting any node as a hub, etc. In these types of configurations, the hubs serve as connection point between 2 installations, allowing, in this way, to replace a large amount of direct connections between all pair of the nodes, therefore, minimizing the total transportation cost of the network. Thus, this work proposes a 2‐stage metaheuristic based on the combination of biased‐randomized technique with an iterated local search framework for solving the uncapacitated single allocation p ‐hub median problem, with computational results that validate the methodology for large‐size instances from the literature.