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Solving multi-objective hub location problems by hybrid algorithms
In many logistic, telecommunications and computer networks, direct routing of
commodities between any origin and destination is not viable due to economic and technolog-
ical constraints. In that cases, a network with centralized units, known as hub facilities, and a
small number of links is commonly used to connect any origin-destination pair. The purpose
of these hub facilities is to consolidate, sort and transship e ciently any commodity in the
network. Hub location problems (HLPs) consider the design of these networks by locating a
set of hub facilities, establishing an interhub subnet, and routing the commodities through
the network while optimizing some objective(s) based on the cost or service.
Hub location has evolved into a rich research area, where a huge number of papers have
been published since the seminal work of O'Kelly [1]. Early works were focused on analogue
facility location problems, considering some assumptions to simplify network design. Recent
works [2] have studied more complex models that relax some of these assumptions and in-
corporate additional real-life features. In most HLPs considered in the literature, the input
parameters are assumed to be known and deterministic. However, in practice, this assumption
is unrealistic since there is a high uncertainty on relevant parameters, such as costs, demands
or even distances.
In this work, we will study the multi-objective hub location problems with uncertainty.Universidad de MĂĄlaga. Campus de Excelencia Internacional AndalucĂa Tec
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