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.
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