Armin Lüer-Villagra | Searcharxiv

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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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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