Cristián E. Cortés

Bio: Cristián E. Cortés is an academic researcher from University of Chile. The author has contributed to research in topics: Model predictive control & Stars. The author has an hindex of 28, co-authored 107 publications receiving 2451 citations. Previous affiliations of Cristián E. Cortés include Metropolitan University & Federal University of Rio Grande do Norte.

The pickup and delivery problem with transfers: Formulation and a branch-and-cut solution method

Abstract: In this paper, a strict formulation of a generalization of the classical pickup and delivery problem is presented Here, we add the flexibility of providing the option for passengers to transfer from one vehicle to another at specific locations As part of the mathematical formulation, we include transfer nodes where vehicles may interact interchanging passengers Additional variables to keep track of customers along their route are considered The formulation has been proven to work correctly, and by means of a simple example instance, we conclude that there exist some configurations in which a scheme allowing transfers results in better quality optimal solutions Finally, a solution method based on Benders decomposition is addressed We compare the computational effort of this application with a straight branch and bound strategy; we also provide insights to develop more efficient set partitioning formulations and associated algorithms for solving real-size problems

Helium enhanced stars and multiple populations along the horizontal branch of NGC 2808: direct spectroscopic measurements

Abstract: We present an abundance analysis of 96 horizontal branch (HB) stars in NGC 2808, a globular cluster exhibiting a complex multiple stellar population p attern. These stars are distributed in different portions of the HB and cover a wide range of temperature. By studying the chemical abundances of this sample, we explore the connection between HB morphology and the chemical enrichment history of multiple stellar populatio ns. For stars lying on the red HB, we use GIRAFFE and UVES spectra to determine Na, Mg, Si, Ca, Sc, Ti, Cr, Mn, Fe, Ni, Zn, Y, Ba, and Nd abundances. For colder, blue HB stars, we derive abundances for Na, primarily from GIRAFFE spectra. We were also able to measure direct NLTE He abundances for a subset of these blue HB stars with temperature higher than∼9000 K. Our results show that: (i) HB stars in NGC 2808 show different content in Na depending on their position in the color-magnitude diagram, with blue HB stars having higher Na than red HB stars; (ii) the red HB is not consistent with an uniform chemical abundance, with slightly warmer stars exhibiting a statistically significant higher Na content; and (iii) our subsample of blue HB stars with He abundances shows evidence of enhancement with respect to the predicted primordial He ‐ ‐ ‐ ‐

Hybrid predictive control for real-time optimization of public transport systems' operations based on evolutionary multi-objective optimization

Abstract: A hybrid predictive control formulation based on evolutionary multi-objective optimization to optimize real-time operations of public transport systems is presented. The state space model includes bus position, expected load and arrival time at stops. The system is based on discrete events, and the possible operator control actions are: holding vehicles at stations and skipping some stations. The controller (operator) pursues the minimization of a dynamic objective function to generate better operational decisions under uncertain demand at bus stops. In this work, a multi-objective approach is conducted to include different goals in the optimization process that could be opposite. In this case, the optimization was defined in terms of two objectives: waiting time minimization on one side, and impact of the strategies on the other. A genetic algorithm method is proposed to solve the multi-objective dynamic problem. From the conducted experiments considering a single bus line corridor, we found that the two objectives are opposite but with a certain degree of overlapping, in the sense that in all cases both objectives significantly improve the level of service with respect to the open-loop scenario by regularizing the headways. On average, the observed trade-off validates the proposed multi-objective methodology for the studied system, allowing dynamically finding the pseudo-optimal Pareto front and making real-time decisions based on different optimization criteria reflected in the proposed objective function compounds.

Integrating short turning and deadheading in the optimization of transit services

Abstract: Urban transit demand exhibits peaks in time and space, which can be efficiently served by means of different fleets, increasing frequencies in those groups of stops with larger passenger inflow. In this paper we develop a model that combines short turning and deadheading in an integrated strategy for a single transit line, where the optimization variables are both of a continuous and discrete nature: frequencies within and outside the high demand zone, vehicle capacities, and those stations where the strategy begins and ends. We show that closed solutions can be obtained for frequencies in some cases, which resembles the classical “square root rule”. Unlike the existing literature that compares different strategies with a given normal operation (no strategy – single frequency), we use an optimized base case, in order to assess the potential benefits of the integrated strategy on a fair basis. We found that the integrated strategy can be justified in many cases with mixed load patterns, where unbalances within and between directions are observed. In general, the short turning strategy may yield large benefits in terms of total cost reductions, while low benefits are associated with deadheading, due to the extra cost of running empty vehicles in some sections.

Hybrid adaptive predictive control for the multi-vehicle dynamic pick-up and delivery problem based on genetic algorithms and fuzzy clustering

Abstract: In this paper, we develop a family of solution algorithms based upon computational intelligence for solving the dynamic multi-vehicle pick-up and delivery problem formulated under a hybrid predictive adaptive control scheme. The scheme considers future demand and prediction of expected waiting and travel times experienced by customers. In addition, this work includes an analytical formulation of the proposed prediction models that allow us to search over a reduced feasible space. Predictive models consider relevant state space variables as vehicle load and departure time at stops. A generic expression of the system cost function is used to measure the benefits in dispatching decisions of the proposed scheme when solving for more than two-step ahead under unknown demand. The demand prediction is based on a systematic fuzzy clustering methodology, resulting in appropriate call probabilities for uncertain future. As the dynamic multi-vehicle routing problem considered is NP-hard, we propose the use of genetic algorithms (GA) that provide near-optimal solutions for the three, two and one-step ahead problems. Promising results in terms of computation time and accuracy are presented through a simulated numerical example that includes the analysis of the proposed fuzzy clustering, and the comparison of myopic and new predictive approaches solved with GA.

Optimization for dynamic ride-sharing: A review

Abstract: Dynamic ride-share systems aim to bring together travelers with similar itineraries and time schedules on short-notice. These systems may provide significant societal and environmental benefits by reducing the number of cars used for personal travel and improving the utilization of available seat capacity. Effective and efficient optimization technology that matches drivers and riders in real-time is one of the necessary components for a successful dynamic ride-share system. We systematically outline the optimization challenges that arise when developing technology to support ride-sharing and survey the related operations research models in the academic literature. We hope that this paper will encourage more research by the transportation science and logistics community in this exciting, emerging area of public transportation.

What about the messenger

Abstract: Recently NAFU SA and other role players expressed some criticism about government programmes. The criticism was not so much about the objectives and content of these programmes, but rather about their accessibility, or lack thereof, to emerging farmers.

The vehicle routing problem

Abstract: Taxonomic review of vehicle routing literature published between 2009 and mid 2015.An adapted version of an existing comprehensive taxonomy is presented.277 VRP articles are classified based on problem characteristics and assumptions.The classification table allows researchers to quickly find relevant literature.Recent trends in VRP literature are discussed. Over the past decades, the Vehicle Routing Problem (VRP) and its variants have grown ever more popular in the academic literature. Yet, the problem characteristics and assumptions vary widely and few literature reviews have made an effort to classify the existing articles accordingly. In this article, we present a taxonomic review of the VRP literature published between 2009 and June 2015. Based on an adapted version of an existing comprehensive taxonomy, we classify 277 articles and analyze the trends in the VRP literature. This classification is the first to categorize the articles to this level of detail.