A bibliometric review and analysis of traffic lights optimization

The significant increase in the number of vehicles in urban areas emerges the challenge of urban mobility. Researchers in this area suggest that most daily delays in urban travel times are caused by intersections, which could be reduced if the traffic lights at these intersections were more efficient. The use of simulation for real intersections can be effective in optimizing the cycle times and improving the traffic light timing to coordinate vehicles passing through intersections. From these themes emerge the research questions: How are the existing approaches (optimization techniques and simulation) to managing traffic lights smartly? What kind of data (offline and online) are used for traffic lights optimization? How beneficial is it to propose an optimization approach to the traffic system? This paper aims to answer these questions, carried out through a bibliometric literature review. In total, 93 articles were analyzed. The main findings revealed that the United States and China are the countries with the most studies published in the last ten years. Moreover, Particle Swarm Optimization is a frequently used approach, and there is a tendency for studies to perform optimization of real cases by real-time data, showing that the praxis of smart cities has resorted to smart traffic lights.This work has been supported by FCT— Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020 and the project “Integrated and Innovative Solutions for the well-being of people in complex urban centers” within the Project Scope NORTE-01-0145-FEDER-000086

Safe Learning and Optimization Techniques: Towards a Survey of the State of the Art

Safe learning and optimization deals with learning and optimization problems that avoid, as much as possible, the evaluation of non-safe input points, which are solutions, policies, or strategies that cause an irrecoverable loss (e.g., breakage of a machine or equipment, or life threat). Although a comprehensive survey of safe reinforcement learning algorithms was published in 2015, a number of new algorithms have been proposed thereafter, and related works in active learning and in optimization were not considered. This paper reviews those algorithms from a number of domains including reinforcement learning, Gaussian process regression and classification, evolutionary algorithms, and active learning. We provide the fundamental concepts on which the reviewed algorithms are based and a characterization of the individual algorithms. We conclude by explaining how the algorithms are connected and suggestions for future research.Comment: The final authenticated publication was made In: Heintz F., Milano M., O'Sullivan B. (eds) Trustworthy AI - Integrating Learning, Optimization and Reasoning. TAILOR 2020. Lecture Notes in Computer Science, vol 12641. Springer, Cham. The final authenticated publication is available online at \<https://doi.org/10.1007/978-3-030-73959-1_12

Simulation-Optimization model for a hybrid flow shop. Case study, chemical industry

Actualmente, las técnicas de investigación de operaciones han demostrado tener un impacto significativo en los sistemas de fabricación modernos, ya que proporcionan una productividad y rendimiento mejorados en mercados altamente competitivos. El problema del Flow Shop de flujo híbrido, también conocido como problema de Flow Shop flexible, es un problema de programación relacionado con un grupo de máquinas paralelas por etapa, frecuentemente asociado con la minimización del tiempo en un entorno de producción. Este problema se considera un problema NP-hard debido a las decisiones combinatorias, los recursos informáticos exigentes y el tiempo de ejecución. Esta investigación se centra en un caso de estudio de la empresa Fuller Pinto, una empresa internacional con sede en Colombia de la industria química que presenta un entorno de tienda de flujo híbrido. Incluso hoy en día, la compañía todavía tiene problemas asociados con la entrega tardía de productos debido a la mala programación, lo que hace que la producción planificada no se lleve a cabo en su totalidad. Además, estos productos sin terminar se convierten en pedidos pendientes con mayor importancia que deben suministrarse de manera obligatoria. Por esta razón, esta investigación propone un sistema de apoyo a la decisión basado en un modelo de simulación-optimizacion para la programación de Fuller Pinto, con el objetivo de minimizar la tardanza total ponderada. El modelo propuesto presenta capacidades mejoradas que simulan el comportamiento del entorno de fabricación para soportar las decisiones de Fuller Pinto. Para validar este modelo, se han probado diferentes escenarios o instancias relacionadas con el comportamiento de producción de productos químicos. Se presentan las comparaciones entre el modelo de simulación-optimización propuesto, la regla de despacho de SPT y los datos históricos de la empresa. Como resultado, el modelo proporciona una programación de la producción óptima de trabajos para cada campaña de Fuller Pinto.Currently, opeiations research techniques have proved to make a significant impact in modem manufacturing systeins as it provides an enhanced productivity and performance on highly competitive markets. Hybrid Flow Shop Problem, also known as Flexible Flow Shop problem, is a scheduling problem related to a group ofparallel machines per stage, frequently associated with time minimization in a manufacturing environment. This problem is considered a NP-hard problem due to the combinatoria! decisions and the demanding computing resources and execution time in its resolution. This research is focused on a case study of Fuller Pinto which is an intemational Colombian-based company from the Chemical industry that presents a Hybrid Flow Shop environment on its shop-floor. Even today, the company still have issues associated to late deliverof products due to poor scheduling, causing the planned production no to be completed. Moreover, these unfínished products become backorders with higher importance that must be supplied mandatory. For this reason, this research proposes a decisión support system based on a simulation-optimization model íor Fuller Pinto scheduling, aiming the minimization of the total weighted tardiness. The proposed model features enhanced capabilities simulating the behavior of the complex manufacturing environment and hybridize iteratively an optimization algorithm to support the Fuller Pinto decisions. To valídate this model, different scenarios or instances related to the production behavior of Chemical products have been tested. Comparisons among the proposed simulation-optimization model, the SPT dispatching rule and historical data of the company are presented. As results, the model provides an optimal schedule of jobs for each campaign of Fuller Pinto.Ingeniero (a) IndustrialPregrad

Reliable Simulation-Optimization of Traffic Lights in a Real-World City

In smart cities, when the real-time control of traffic lights is not possible, the global optimization of traffic-light programs (TLPs) requires the simulation of a traffic scenario (traffic flows across the whole city) that is estimated after collecting data from sensors at the street level. However, the highly dynamic traffic of a city means that no single traffic scenario is a precise representation of the real system, and the fitness of any candidate solution (traffic-light program) will vary when deployed on the city. Thus, ideal TLPs should not only have an optimized fitness, but also a high reliability, i.e., low fitness variance, against the uncertainties of the real-world. Earlier traffic-light optimization methods, e.g., based on genetic algorithms, often simulate a single traffic scenario, which neglects variance in the real-world, leading to TLPs not optimized for reliability. Our main contributions in this work are the following: (a) the analysis of the importance of reliable solutions for TLP optimization, even when all traffic scenarios are consistent with the real-world data and highly correlated; (b) the adaptation of irace, an iterated racing algorithm that is able to dynamically adjust the number of traffic scenarios required to evaluate the fitness of TLPs and their reliability; (c) the use of a large real-world case study for which real-time control is not possible and where data was obtained from sensors at the street level; and (d) a thorough analysis of solutions generated by means of irace, a Genetic Algorithm, a Differential Evolution, a Particle Swarm Optimization and a Random Search. This analysis shows that simple strategies that simulate multiple traffic scenarios are able to obtain optimized solutions with improved reliability; however, the best results are obtained by irace, among the algorithms evaluated.This research has been partially funded by the Spanish Min istry of Science and Innovation and FEDER under contract TIN2017-88213-R (6city), the network of smart cities CI-RTI (TIN2016-81766-REDT), the EcoIoT project (RTC-2017-6714-5), and the CELTIC C2017/2-2 project in collaboration with com panies EMERGYA and SECMOTIC with contracts #8.06/5.47.4997 and #8.06/5.47.4996. Part of this work was carried out while M. López-Ibáñez was a visiting researcher at the NEO group thanks to the support of a grant (‘‘Estancias Tipo B, Fondos Propios UMA 2014’’) from the University of Málaga. J.Ferrer thanks Uni versity of Málaga for his postdoc fellowship. We also thank Daniel Stolfi for generating the traffic scenario files from the sensor dat