Mercedes Landete

The p -hub median problem is central to location science, with multiple applications in network design. Given a set of nodes, each of them sends and receives some flow to and from the other nodes. Among these nodes, p have to be selected to serve as hubs, which act as switching points for directing the flow. These hubs are interconnected with direct links to form a complete subnetwork, while every non-hub node is linked to one of these selected hubs. The p -hub median problem typically arises in the modeling of air traffic, disaster management, logistics, and telecommunications networks, among others. In a world driven by dynamic data, situations may arise in which the same p -hub median problem must be solved for multiple instances with varying flows. While solving each instance exactly from scratch may require long computational times, learning from a training set of previously solved instances can help predict near-optimal solutions for new ones in which the flows have been changed. In this setting, we propose two heuristics to address the p -hub median location problem with single allocation and unconstrained hub capacity: one for problems in which all hub links are activated, allowing flow exchange between every pair of hubs, and another for problems in which flow exchanges between hubs are limited because not all hub links are activated. For the case where all hubs exchange flow with each other, the set of nodes acting as hubs is predicted using a machine learning approach. In the case where flow exchanges between hubs are limited, both the hubs and the activated connections between hubs are predicted using machine learning. Both heuristics include carefully designed strategies to restore feasibility. Our extensive computational results demonstrate the effectiveness of the proposed heuristics, both in terms of the good quality of the solutions, which are near-optimal, and in their ability to produce them in record time when compared to state-of-the-art approaches.

Moya-Martínez, A., Landete, M., & Monge, J. F. (2026). Una aplicación al problema de localización de servicios en el sistema de salud de Elche. Boletín de Estadística e Investigación Operativa, 42(1), 24-32. https://doi.org/10.63552/beio.2026.42.1.03

Rank aggregation problems aim to combine multiple individual orderings of a common set of items into a consensus ranking that best reflects the collective preferences. This paper introduces a general Integer Linear Programming (ILP) framework to model and solve, in an exact way, problems whose solutions are weak orders (a.k.a.\ bucket orders). Within this framework, we consider additional relevant constraints to produce the consensus bucket order, considering configurations with a fixed number of buckets, predefined bucket sizes, top-$k$ type problems, and fairness constraints. All these formulations are developed in a general setting, allowing their application to different rank aggregation contexts. One of these problems is the Optimal Bucket Order Problem (OBOP), for which we propose for the first time an exact formulation and test the variants proposed. The computational study includes, on the one hand, a comparison between the exact results obtained by our models and the heuristic methods proposed by Aledo et al.\ (2018), and on the other hand, an additional evaluation of their performance on a representative set of instances from the PrefLib library. The results confirm the validity and efficiency of the proposed approach, providing a solid foundation for future research on rank aggregation problems with weak orders as consensus rankings.

The Capacitated Vehicle Routing Problem with Planned Downtime introduced in this work is a generalization of the classical vehicle routing problem in which two sets of routes have to be jointly designed: one set considering that all the vehicles of the fleet are available, and another set that considers the scheduled unavailability of a vehicle. Besides routing cost minimization, it is also intended that the routes for the two cases (when all vehicles are available and when one is missing) are as similar as possible in order to reduce the impact on the customers. In this paper, we formally define the problem, propose two mixed integer programming models for it, one of them based on bilevel programming, and devise exact branch-and-cut algorithms based on those models. The algorithms are first compared, and then the most efficient one is tested on different benchmark instances with up to 40 nodes and 5 vehicles.