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Paper published: Trajectory stability in the traveling salesman problem

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Two generalizations of the traveling salesman problem in which sites change their position in time are presented. The way the rank of different trajectory lengths changes in time is studied using the rank diversity. We analyze the statistical properties of rank distributions and rank dynamics and give evidence that the shortest and longest trajectories are more predictable and robust to change, that is, more stable.

Sánchez, S., Cocho, G., Flores, J., Gershenson, C., Iñiguez, G., and Pineda, C. (2018). Trajectory stability in the traveling salesman problem. Complexity, 2018:2826082. https://doi.org/10.1155/2018/2826082


Tenure-track Research Professor in Data Science at UNAM Mérida

The Computer Science Department of the Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas (IIMAS) of the Universidad Nacional Autónoma de México (UNAM) has a open call for a research professor in data science for the new UNAM campus in Mérida, Yucatán. This position, aimed at young researchers, consists of renewable one-year contracts with the possibility of tenure after three years.

Application deadline: February 23, 2018.

More information
Dr. Edgar Garduño
Head of Computer Science Department
edgargar AT unam DOT mx

Paper published: Improving public transportation systems with self-organization: A headway-based model and regulation of passenger alighting and boarding

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The equal headway instability—the fact that a configuration with regular time intervals between vehicles tends to be volatile—is a common regulation problem in public transportation systems. An unsatisfactory regulation results in low efficiency and possible collapses of the service. Computational simulations have shown that self-organizing methods can regulate the headway adaptively beyond the theoretical optimum. In this work, we develop a computer simulation for metro systems fed with real data from the Mexico City Metro to test the current regulatory method with a novel self-organizing approach. The current model considers overall system’s data such as minimum and maximum waiting times at stations, while the self-organizing method regulates the headway in a decentralized manner using local information such as the passenger’s inflow and the positions of neighboring trains. The simulation shows that the self-organizing method improves the performance over the current one as it adapt…