Search

In this paper, a modeling framework is proposed for multi-period hub location. The problems to be studied are extensions of classical hub location problems to the situation in which the hub network can be progressively built and its capacity gradually expanded over time. Both the single allocation and the multiple allocation cases are considered. For each case, a mixed-integer linear programming formulation is proposed and a set of valid inequalities is derived for enhancing the corresponding model. The results of a set of computational tests performed using the formulations proposed and their enhancements are reported. The value of the multi-period solution is discussed as a measure for evaluating the relevance of considering a multi-period model instead of a static counterpart. [ABSTRACT FROM AUTHOR]

In this paper we extend the classical capacitated single-allocation hub location problem by considering that multiple products are to be shipped through the network. We propose a unified modeling framework for the situation in which no more than one hub can be located in each node. In particular, we consider the case in which all hubs are dedicated to handling a single-product as well as the case in which all hubs can handle all products. The objective is to minimize the total cost, which includes setup costs for the hubs, setup costs for each product in each hub and flow routing costs. Hubs are assumed to be capacitated. For this problem several models are proposed which are based on existing formulations for the (single-product) capacitated single-allocation hub location problem. Additionally, several classes of inequalities are proposed in order to strengthen the models in terms of the lower bound provided by the linear relaxation. We report results of a set of computational experiments conducted to test the proposed models and their enhancements. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, an extension of the capacitated single-allocation hub location problem is considered in which the capacity of the hubs is part of the decision making process and balancing requirements are imposed on the network. The decisions to be made comprise (i) the selection of the hubs, (ii) the allocation of the spoke nodes to the hubs, (iii) the flow distribution through the sub network defined by the hubs and (iv) the capacity level at which each hub should operate. In the latter case, for each potential hub, a set of available capacities is considered among which one can be chosen. The objective is to minimize the total cost, which includes the setup cost for the hubs as well as the flow routing cost. Economies of scale are assumed for the costs. Balancing requirements are imposed to the network. In particular, a value is considered for the maximum difference between the maximum and minimum number of spoke nodes that are allocated to the hubs. Two mixed-integer linear programming formulations are proposed and analyzed for this problem. The results of a set of computational experiments using an off-the-shelf commercial solver are presented. These tests aim at evaluate the possibility of solving the problem to optimality using such a solver with a particular emphasis to the impact of the balancing requirements. The tests also allow an analysis of the gap of the bounds provided by linear relaxation. [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]