arXiv subjects

Elena Fernández

Publications and source records attributed to Elena Fernández.

11 recordsLinked to original sources

arXiv2026

Optimizing Accessibility and Maximum Travel Times in Transit Networks: The Hub Covering Location Problem

This work introduces an optimization model to support decision-making in the design of integrated transit systems, with the aim of improving urban mobility. We assume that daily user trips consist of two types of segments: short segments where users apply their own transport between their homes or workplaces and the public transit access points, and a longer segment on the transportation network. Users' accessibility is ensured by imposing an upper limit on the travel times of the short segments. We assume that only users within a given coverage radius will employ the system. On the other hand, system effectiveness is ensured by limiting travel time on the transportation network. Two mixed-integer linear programming formulations and a matheuristic procedure are proposed, and their efficiency is evaluated through computational experiments. The results allow us to study the structure of the solutions obtained and analyze the effects of the parameters considered.

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arXiv2025

A strong formulation for Multiple Allocation Hub Location based on supermodular inequalities

We introduce a new formulation for the multiple allocation hub location problem that exploits supermodular properties and uses 1- and 2-index variables only. We show that the new formulation produces the same Linear Programming bound as the tightest existing formulations for the studied problem, which use 4-index variables, outperforming existing supermodular formulations adapted to the considered problem. Computational results are presented with instances of up to 200 nodes optimally solved within a time limit of two hours.

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arXiv2025

Branch & Solve for Hub Location

This paper introduces a new formulation and solution framework for hub location problems. The formulation is based on 2-index aggregated flow variables and incorporates a set of aggregated demand constraints, which are novel in hub location. With minor adaptations, the approach applies to a large class of single- and multiple-allocation models, possibly incorporating flow bounds on activated arcs. General-purpose feasibility and optimality inequalities are also developed. Because of the small number of continuous variables, there is no need to project them out, differentiating the method from solution algorithms that rely heavily on feasibility and optimality cuts. The proposed Branch & Solve solution framework leverages the nested structure of the problems, by solving auxiliary subproblems at selected nodes of the enumeration tree. Extensive computational experiments on benchmark instances from the literature confirm the good performance of the proposal: the basic version of the algorithm is able to solve to proven optimality instances with up to 200 nodes for several hub location families.

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arXiv2025

On the $(k,\ell)$-multiset anonymity measure for social graphs

The publication of social graphs must be preceded by a rigorous analysis of privacy threats against social graph users. When the threat comes from inside the social network itself, the threat is called an active attack, and the de-facto privacy measure used to quantify the resistance to such an attack is the $(k,\ell)$-anonymity. The original formulation of $(k,\ell)$-anonymity represents the adversary's knowledge as a vector of distances to the set of attacker nodes. In this article, we argue that such adversary is too strong when it comes to counteracting active attacks. We, instead, propose a new formulation where the adversary's knowledge is the multiset of distances to the set of attacker nodes. The goal of this article is to study the $(k,\ell)$-multiset anonymity from a graph theoretical point of view, while establishing its relationship to $(k,\ell)$-anonymity in one hand, and considering the $k$-multiset antiresolving sets as its theoretical frame, in a second one. That is, we prove properties of some graph families in relation to whether they contain a set of attacker nodes that breaks the $(k,\ell)$-multiset anonymity. From a practical point of view, we develop a linear programming formulation of the $k$-multiset antiresolving sets that allows us to calculate the resistance of social graphs against active attacks. This is useful for analysts who wish to know the level of privacy offered by a graph.

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arXiv2024

Hub location with congestion and time-sensitive demand

This work studies the effect of hub congestion and time-sensitive demand on a hub-and-spoke location/allocation system. The Hub Location with Congestion and Time-sensitive Demand Problem is introduced, which combines these two main characteristics. On the one hand, hubs can be activated at several service levels, each of them characterized by a maximum capacity, expressed as the amount of flow that may circulate through the hub, which is associated with a hub transit time. On the other hand, alternative levels are available for served commodities, where each demand level is characterized by its amount of demand, unit revenue, and maximum service time. In this problem the efficiency of a hub-and-spoke system is given by the maximum net profit it may produce. To the best of our knowledge this is the first work where hub congestion and time-sensitive demand are jointly considered. Two alternative mixed-integer linear programming formulations are proposed. They include a new set of constraints, which are necessary to guarantee the consistency of the obtained solutions under the presence of the capacity-type constraints derived from hub service levels and served demand levels. The efficiency of the formulations is analyzed through a set of computational experiments. The results of the computational experiments allow to study the structure of the obtained solutions and to analyze how the different parameters affect them.

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arXiv2023

Outsourcing policies for the Facility Location Problem with Bernoulli Demand

This paper focuses on the Facility Location Problem with Bernoulli Demand, a discrete facility location problem with uncertainty where the joint distribution of the customers' demands is expressed by means of a set of possible scenarios. A two-stage stochastic program with recourse is used to select the facility locations and the a priori assignments of customers to open plants, together with the a posteriori strategy to apply in those realizations where the a priori solution is not feasible. Four alternative outsourcing policies are studied for the recourse action, and a mathematical programming formulation is presented for each of them. Extensive computational experiments have been carried-out to analyze the performance of each of the formulations and to compare the quality of the solutions produced by each of them relative to the other outsourcing policies.

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arXiv2023

On the (k,l)-anonymity of networks via their $k$-metric antidimension

This work focuses on the (k,l)-anonymity of some networks as a measure of their privacy against active attacks. Two different types of networks are considered. The first one consists of graphs with a predetermined structure, namely cylinders, toruses, and $2$-dimensional Hamming graphs, whereas the second one is formed by randomly generated graphs. In order to evaluate the (k,l)-anonymity of the considered graphs, we have computed their k-metric antidimension. To this end, we have taken a combinatorial approach for the graphs with a predetermined structure, whereas for randomly generated graphs we have developed an integer programming formulation and computationally tested its implementation. The results of the combinatorial approach, as well as those from the implementations indicate that, according to the (k,l)-anonymity measure, only the 2-dimensional Hamming graphs and some general random dense graphs are achieving some higher privacy properties.

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arXiv2022

Hub Location with Protection under Link Failures

This paper introduces the Hub Location Problem under Link Failures, a hub location problem in which activated inter-hub links may fail with a given probability. Two different optimization models are studied, which construct hub backbone networks protected under hub links disruptions by imposing that for each commodity an additional routing path exists besides its original routing path. Both models consider the minimization of the set-up costs of the activated hubs and inter-hub links plus the expected value of the routing costs of the original and alternative paths. The first model builds explicitly the alternative routing paths, while the second model guarantees that for each commodity at least one alternative path exists using a large set of connectivity constraints, although the alternative paths are not built explicitly. The results of extensive computational testing allow to analyze the performance of the two proposed models and to evaluate the extra cost required to design a robust backbone network under hub links failures. The obtained results support the validity of the proposal.

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arXiv2019

On the fuzzy maximal covering location problem

In this paper studies the maximal covering location problem, assuming imprecise knowledge of all data involved. The considered problem is modeled from a fuzzy perspective producing suitable fuzzy Pareto solutions. Some properties of the fuzzy model are studied, which validate the equivalent mixed-binary linear multiobjective formulation proposed. A solution algorithm is developed, based on the augmented weighted Tchebycheff method, which produces solutions of guaranteed Pareto optimality. The effectiveness of the algorithm has been tested with a series of computational experiments, whose numerical results are presented and analyzed

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arXiv2016

Minimum Spanning trees with Neighborhoods

This paper studies Minimum Spanning Trees under incomplete information for its vertices. We assume that no information is available on the precise placement of vertices so that it is only known that vertices belong to some neighborhoods that are second order cone representable and distances are measured with a $\ell_q$-norm. Two mixed integer non linear mathematical programming formulations are presented, based on alternative representations of subtour elimination constraints. A solution scheme is also proposed, resulting from a reformulation suitable for a Benders-like decomposition, which is embedded within an exact branch-and-cut framework. Furthermore, a mathheuristic is developed, which alternates in solving convex subproblems in different solution spaces, and is able to solve larger instances. The results of extensive computational experiments are reported and analyzed.

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arXiv2013

A modeling framework for Ordered Weighted Average Combinatorial Optimization

Multiobjective combinatorial optimization deals with problems considering more than one viewpoint or scenario. The problem of aggregating multiple criteria to obtain a globalizing objective function is of special interest when the number of Pareto solutions becomes considerably large or when a single, meaningful solution is required. Ordered Weighted Average or Ordered Median operators are very useful when preferential information is available and objectives are comparable since they assign importance weights not to specific objectives but to their sorted values. In this paper, Ordered Weighted Average optimization problems are studied from a modeling point of view. Alternative integer programming formulations for such problems are presented and their respective domains studied and compared. In addition, their associated polyhedra are studied and some families of facets and new families of valid inequalities presented. The proposed formulations are particularized for two well-known combinatorial optimization problems, namely, shortest path and minimum cost perfect matching, and the results of computational experiments presented and analyzed. These results indicate that the new formulations reinforced with appropriate constraints can be effective for efficiently solving medium to large size instances.

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