This article addresses the task assignment problem in IT service companies, where managers must balance profitability with compliance to customer-defined deadlines. We propose a bi-objective mixed-integer linear programming model that simultaneously maximizes company profit and minimizes deviations from service-level agreements (SLAs). To solve this problem, three exact multiobjective optimization methods were applied and compared: Weighted Sum, classical Epsilon Constraints, and Augmented Epsilon Constraints. Numerical experiments highlight the superiority of the Augmented Epsilon Constraints method, which produced high-quality Pareto fronts (GD = 0, Spread ≈ 0.97) while reducing runtime by more than 97

This article discusses the utilization of Artificial Neural Networks for predicting wind power generation in Uruguay. The precise estimation of power output plays a vital role in designing a dependable wind power generation infrastructure. Accurate predictions enable the implementation of efficient planning, management, and distribution strategies for the produced power, thereby enhancing the performance and the efficacy of the system. The research incorporates actual wind power generation data from Uruguay spanning the period between 2018 and 2022. The forecasting is performed using a Long Short-Term Memory artificial neural network. The primary findings suggest that the proposed methodology has a good prediction accuracy, with an average root mean square error of 0.12. The computed forecasts can be effectively employed for planning and scheduling to enhance service quality.

The management of power generation systems requires the optimized coordination of resources and investments across timeframes ranging from days to decades. This paper introduces a mixed-integer optimization model (MIP) for the short-term operation of the Río Negro Hydroelectric Complex, a crucial asset in Uruguay’s efforts to achieve energy sovereignty by primarily relying on wind, solar, and hydroelectric power sources. The model addresses the challenge of balancing fluctuating renewable energy supply with hydroelectric resources while ensuring cost-effective dispatch and system reliability. The experimental results demonstrate the accuracy with which a MIP approximation can model an extremely nonlinear problem.

This article explores the application of Artificial Neural Networks in predicting wind power generation. The precise prediction of power output is crucial for establishing a reliable wind power generation framework. Accurate forecasts play a key role in enabling efficient planning, management, and distribution strategies for the generated power, leading to improved performance of electrical systems. A specific case study is addressed: the nation-wide wind power generation in Uruguay. The study considers real wind power generation data from Uruguay, spanning the period from 2018 to 2022. The forecasting is carried out using different artificial neural network architectures, including Convolutional Neural Network, Long Short-Term Memory artificial neural network, Encoder-Decoder architectures, and hybrid models. Univariate and multivariate models are studied. The main results indicate that the best proposed hybrid approach demonstrates high prediction accuracy, with a median root mean square error of 0.12, median absolute error of 0.09, and mean absolute percentage error of 14.9%, significantly improving over other models. The computed forecasts can be effectively utilized for planning and scheduling to enhance the quality of services provided by the smart electric grid.

This paper examines recent advancements in securing Internet routing, focusing on Resource Public Key Infrastructure/Route Origin Validation (RPKI/ROV) and revisiting established mechanisms like Internet Routing Registries (IRRs). We begin by briefly outlining Internet routing, with a particular emphasis on the Border Gateway Protocol (BGP). Following this introduction, we categorize common routing incidents, including leaks and hijacks. Finally, we delve into the techniques and protocols designed to mitigate these security threats, detailing the scenarios for which they are most suitable.

Prefix-classes group network prefixes that are equivalent with respect to the choice of the next-hop border gateway within an Autonomous System (AS). Clustering the routing information is such way significantly reduces the volume of data needed to analyze or design a network, from several million incoming updates to a few hundred classes, while still finding optimal routes. Existing methods for calculating prefix classes rely upon detailed internal data of the AS under study, which is rarely available. This research proposes a new stochastic method to estimate prefix classes for any AS using only publicly available information. This probabilistic model was tested on a real network and achieved a high degree of accuracy.

This article describes an approach applying computational intelligence methods for the problem of forecasting solar photovoltaic power generation at country level. Precise forecast of power generation plays a vital role in designing a dependable photovoltaic power generation system. The computed predictions enable the implementation of efficient planning, management, and distribution strategies for the generated power, ultimately enhancing the performance and efficiency of the system. The study analyzes and compares artificial neural network approaches for a specific case study using real solar photovoltaic power generation data from Uruguay in the period 2018 to 2022. Several artificial neural network architectures are evaluated for forecasting. The main results indicate that the approach applying a combination of Encoder-Decoder and Long Short Term Memory artificial neural networks is the most effective method for the addressed forecasting problem. The approach yielded promising results, with an average mean error value of 0.09, improving over the other artificial neural network architectures. Even better results were obtained for sunny days. The generated forecasts hold significant value for its application in planning and scheduling processes, aiming to enhance the overall quality of service of the electricity grid.

Modern cities heavily rely on public transport systems to enhance citizen access to urban services and promote sustainability. To optimize public transport, intelligent computer-aided tools play a pivotal role in decision making. This article tackles the complex challenge of bus timetabling, specifically focusing on improving multi-leg trips or transfers. It introduces a novel multi-objective Mixed-Integer Programming Linear (MILP) model that concurrently maximizes passenger transfers and minimizes budgetary costs, while also adhering to the minimum required quality-of-service constraints for regular (non-multi-leg) trips, and an exact resolution approach based on the ε-constraint method to obtain a set of efficient solutions is used. The competitiveness of the model is validated via a computational experimentation performed over real-world scenarios from the public transportation system of Montevideo, Uruguay. The findings evinced that the MILP model was able to compute a set of Pareto efficient solutions that explore the tradeoff between the number of successful transfers and the cost of the system. Moreover, the best tradeoff solutions surpass the current city timetable, excelling in both the number of transfers and cost efficiency.

This article presents the application of exact and metaheuristic approaches to the problem of designing the backbone network of a hierarchically public transportation system for Montevideo, Uruguay. This is a very relevant problem in nowadays smart cities, as it accounts for many social and environmental impacts and also affects the dynamics of the cities. The design of the proposed backbone network is conceived in combination with the bus network, with the main objective of improving the overall quality of service and reducing travel times. Three different variants of the problem are solved, considering different design premises. Exact solvers are proposed for simpler variants of the problem, which account for maximum resilience and bounded travel times. An evolutionary algorithm is proposed for a multiobjectie version of the problem that optimizes cost and quality of service. The main results indicate that the computed optimized designs provide reduced end-to-end travel times, which improve up to five times over the current system, and are economically viable to be implemented.

Providing an efficient public transportation system is a key issue to increase the livability and sustainability of modern cities. This article addresses the bus timetabling problem for enhancing multi-leg trips or transfers. For this purpose, a mixed-integer programming model is proposed, aimed at maximizing the amount of transfers while considering budgetary and quality of service constraints. The proposed model is evaluated on real scenarios from the case study of the public transportation system in Montevideo, Uruguay. Results indicate that the solutions of the proposed model outperforms the current timetable used in the city in terms of number of transfers, cost, and number of required buses.

This article presents a metaheuristic resolution approach for a variant of the Vehicle Routing Problem considering heterogeneous fleet and flexible time windows. This problem variant solved considers extended time windows for delivering products to customers, modeling a realistic situation for logistics in smart cities. The proposed metaheuristic follows an hybrid approach, combining well known search procedures. Accurate results are reported for problem instances built by extending existing benchmarks in the literature. The proposed model is competitive with previous results and was able to compute better solutions in ten problem instances.

This article introduces the main ideas behind prefixes equivalence classes, a process that allows essential information distributed through millions of Internet BGP updates to be captured in a few dozen data records. The technique was successfully used in the past as a means to tackle the problem of optimizing iBGP overlays for a real-world application case, a South American Internet Service Provider (ISP), by then in the early stages of the deployment of its international infrastructure. Recent experimental results performed over a larger network that could potentially lead to millions of classes show that prefixes classes technique preserves its remarkable behavior. In addition, there is evidence that such performance is a consequence of the intrinsic structure of the Internet and such results could be replicated to other regional ISPs.

This article presents an exact approach for solving the problem of locating electric vehicle charging stations in a city, whose goal is upon minimizing the distance citizens must span to charge their vehicles. Mixed integer programming formulations are presented for two variants of the problem: relaxed (i.e., without considering electrical constraints for the infrastructure) and full versions. The experimental evaluation is performed over a real-world case study defined in Málaga, Spain. Results show that the proposed approach can deal with the large number of variables (i.e., millions) of the problem, computing optimal solutions for all problem instances and variants addressed. The improvements in solutions quality over a previous metaheuristic approach applied to the same problem and application case are notorious.

This article introduces the main ideas behind prefixes equivalence classes, a process that allows essential information distributed through millions of Internet BGP updates to be captured in a few dozen data records. The technique was successfully used in the past as a means to tackle the problem of optimizing iBGP overlays for a real-world application case, a South American Internet Service Provider (ISP), by then in the early stages of the deployment of its international infrastructure. Recent experimental results performed over a larger network that could potentially lead to millions of classes show that prefixes classes technique preserves its remarkable behavior. In addition, there is evidence that such performance is a consequence of the intrinsic structure of the Internet and such results could be replicated to other regional ISPs.

This article addresses timetable synchronization in public transportation, an important problem in modern smart cities, in order to guarantee a proper quality of service to citizens. Two variants of the bus timetabling synchronization problem considering extended transfer zones are studied: optimizing offsets and optimizing offsets and headways for each line. An exact mixed integer programming and an evolutionary algorithm are developed to solve both problem variants. The algorithms are evaluated on 45 instances of a real case study, the intelligent transportation system of Montevideo, Uruguay. Experimental results reported significant improvements over the current timetable implemented by the city administration. The number of successful synchronizations improved up to 66.6% and 179.9% for the first and second problem variant, respectively. The average waiting times for transfers improved, especially in tight problem instances (up to 57.8% and 158.3% for the first and second problem variant, respectively). The proposed planning methods are useful to help decision makers to configure public transportation systems.

The increasing rate of penetration of non-conventional renewable energies is affecting the traditional assumption of controllability over energy sources. Power dispatch scheduling methods need to integrate the intrinsic randomness of some new sources, among which, wind energy is particularly difficult to treat. This work aims at the optimal construction of energy bands around wind energy forecasts. Complementarily, a remarkable fact of the proposed technique is that it can be extended to integrate multiple forecasts into a single one, whose band width is narrower at the same level of confidence. The work is based upon a real-world application case, developed for the Uruguayan Electricity Market, a world leader in the penetration of renewable energies.

The Internet is a collection of interconnected Autonomous Systems (ASes) that use the Border Gateway Protocol (BGP) to exchange reachability information. In this regard, BGP stability and scalability in the inter-domain scope have been matters of major concern for many years, and network engineers have been applying several techniques to cope with these issues. BGP is also used intra-domain (internal BGP - iBGP), to disseminate reachability information inside each AS, and works together with the Interior Gateway Protocols (IGPs) such as OSPF or IS-IS, to build routing tables. Route reflection is a widely adopted technique to tackle BGP scalability in the intra-domain scope, and choosing which routers will play the reflector role and which BGP sessions will be established among reflectors and clients (i.e. the routers which are not elected as reflectors), building an overlay of iBGP sessions, is known as the iBGP overlay design problem. The design of an optimal iBGP overlay is known to be a NP-Hard problem, and we proposed solutions for pure IP networks (i.e. best effort traffic forwarding) in our previous work. However, most Internet providers implement their backbones by combining IP routing with MPLS (Multiprotocol Label Switching) for QoS-aware traffic forwarding. MPLS forwarding incorporates traffic engineering and more efficient failover mechanisms; under this traffic forwarding paradigm, the design of traffic-engineered Label Switched Paths (LSPs, also referred as MPLS tunnels) shall be combined with the aforementioned iBGP overlay design. The present work introduces a coordinated design of both the iBGP overlay and the IP/MPLS substrates. Our contribution is the proposal of an optimal and resilient topology design for an IP/MPLS Internet backbone, which takes advantage of traffic engineering features to optimize the demands, while guaranteeing iBGP overlay optimality. We present a complete solution for a real world scenario, and we study the scalability of the solution for synthetic topologies, achieving encouraging results.

This article presents the application of mathematical programming and evolutionary algorithms to solve a variant of the Bus Timetabling Synchronization Problem. A new problem model is proposed to include extended synchronization points, accounting for every pair of bus stops in a city, the transfer demands for each pair of lines, and the offset for lines in the considered scenario. Mixed Integer Programming and evolutionary algorithm are proposed to efficiently solve the problem. A relevant real case study is solved, for the public transportation system of Montevideo, Uruguay. Several scenarios are solved and results are compared with the no-synchronization solution and the current planning of such transportation system too. Experimental results indicate that the proposed approaches are able to significantly improve the current plannings. The Mixed Integer Programming algorithm computed the optimum solution for all scenarios, accounting for an improvement of up to 95

This document analyses the problem of designing a minimum cost local wind turbine grid (or LWTG) for an onshore wind farm. The LWTG is responsible for adding up the power of the farm’s wind turbines to then deliver it to a high voltage network. To minimize disruption of agricultural activities, the cables should be laid along underground conduits, parallel to an existing map of roads in the terrain. Hence, connections of onshore wind farms are limited differently from those in offshore ones. This document presents: the technical constraints of such a grid; a combinatorial model to find cost optimal solutions; and also shows some concrete optimal layouts. The work is based upon a real-world project, the Parque Eólico Palomas , a wind farm in Uruguay, a leader country in the usage of renewable energies and environmental care. The results of this research contributed to reduce investment costs of that project, with savings exceeding 30% over manually crafted solutions.

During the day of October 7-8, the congress has invited experts from the sector of Smart Cities to organize a sectoral debate. This debate will be composed of prestigious companies in the sector, Public Administration, as well as specialized consultants. The aim is to give a business point of view around Smart Cities. CYTED is the Ibero-American Program of Science and Technology for Development, created by the governments of Ibero-American countries to promote cooperation on issues of science, technology and innovation for the harmonious development of Ibero-America. CYTED achieves its objectives through different financing instruments that mobilize Ibero-American entrepreneurs, researchers and experts and allow them to be trained and generate joint research, development and innovation projects. Thus, the countries that make up the CYTED Program are able to keep up to date with the most recent advances and scientific-technological developments.