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Abstract: Hub location problems are network design problems which are solved as part of a strategic decision making process. In strategic planning, decisions may have a long lasting effect and the implementation may take considerable time. Moreover, input data is not precisely known in advance. Hence, decisions have to be made anticipating uncertainty. In this paper, we address several aspects concerning hub location problems under uncertainty. Two sources of uncertainty are considered: the set-up costs for the hubs and the demands to be transported between the nodes. Generic models are presented for single and multiple allocation versions of the problems. Firstly, the two sources of uncertainty are analyzed separately and afterwards a more comprehensive model is proposed considering all sources of uncertainty. Using a set of computational tests performed, we analyze the changes in the solutions driven by the different sources of uncertainty considered isolated and combined. [Copyright &y& Elsevier]

Abstract: In this paper, an extension of the classical capacitated single-allocation hub location problem is studied in which the size of the hubs is part of the decision making process. For each potential hub a set of capacities is assumed to be available among which one can be chosen. Several formulations are proposed for the problem, which are compared in terms of the bound provided by the linear programming relaxation. Different sets of inequalities are proposed to enhance the models. Several preprocessing tests are also presented with the goal of reducing the size of the models for each particular instance. The results of the computational experiments performed using the proposed models are reported. [Copyright &y& Elsevier]

Highlights • A broad family of hub location problems is investigated. • Improved formulations are proposed and discussed. • A polyhedral study is conducted rendering many facet-defining inequalities. • Additional valid inequalities are derived, whose separation is analyzed. • A cutting-plane approach and a branch-and-cut algorithm are devised. Abstract This work focuses on a broad class of uncapacitated p -hub median problems that includes non-stop services and setup costs for the network structures. In order to capture both the single and the multiple allocation patterns as well as any intermediate case of interest, we consider the so-called r -allocation pattern with r denoting the maximum number of hubs a terminal can be allocated to. We start by revisiting an optimization model recently proposed for the problem. For that model, we introduce several families of valid inequalities as well as optimality cuts. Moreover, we consider a relaxation of the model that contains several sets of set packing constraints. This motivates a polyhedral study that we perform and that leads to the identification of many families of facets and other valid inequalities to the relaxed problem that, in turn, provide valid inequalities for the original model. Some of these families are too large for being handled directly. For those cases, separation algorithms are also presented. Finally, we gather all the above elements in a branch-and-cut procedure that we devise and implement for tackling the problem. The methodological developments proposed are tested computationally using data generated from the well-known AP data set. [ABSTRACT FROM AUTHOR]