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El objetivo de este trabajo es estudiar el rendimiento de la adaptación del sistema WLC a entornos DRCFJS. Se trata de un trabajo de investigación sobre control de la producción basados en simulación de eventos discretos.

We analyze several time dependency issues for the selective traveling salesman problem with time-dependent profits. Specifically, we consider the case in which the profit collected at a vertex depends on the service time, understood as the time spent at this vertex, and when the service time at each vertex depends on the arrival time at the vertex. For each of these two cases, we formulate two continuous-time problems: (i) a vertex can be visited at most once, and (ii) vertices may be visited more than once. In each case, we consider general profit functions at the vertices, i.e., the profit functions are not limited to monotonic functions of time. We also formulate the problems as discrete-time problems using appropriate variants of an auxiliary time-extended graph, and we solve them with Gurobi. We apply our methodology to two sets of instances. First, we use a set of artificial instances to illustrate the main differences amongst the different versions of the problem. We then solve several instances adapted from TSPLIB to evaluate the computational capabilities of the methodology.

En los últimos años se han realizados cambios en la mentalidad urbanística de las diferentes ciudades [1], que en conjunto con la concienciación de la problemática ambiental, han impulsado a las personas a emplear sistemas de transportes alternativos a los convencionales. Por ello, es necesario que las ciudades cuenten con una infraestructura que permita a los usuarios emplear medios de transportes alternativos, sin perjudicar la infraestructura de los sistemas de transportes actuales. Estas infraestructuras (carriles para bicicletas) deben cumplir con las normativas de seguridad, las normativas de urbanismo y al mismo tiempo deben ser adecuados para la práctica del ciclismo [2], es por ello por lo que su diseño se ha convertido en una tarea importante. Desde un punto de vista científico, este diseño es posible gracias a la implementación de modelos matemáticos que permiten describir y analizar sistemas o situaciones reales mediante el modelado de las relaciones entre sus elementos. En el presente Trabajo de Fin de Grado se propone una metodología que permite resolver el problema de diseño de una red de carriles de bici teniendo en cuenta el nivel de satisfacción de los usuarios, así como la implementación de múltiples tipos de infraestructuras, y se ilustra su aplicación usando la red de la ciudad de Sevilla, España., In recent years, changes have been made in the urban mentality of the different cities [1], together with the awareness of environmental problems, have encouraged people to use alternative transport systems to conventional ones. Therefore, it is necessary for cities to have an infrastructure that allows users to use alternative means of transport, without harming the infrastructure of current transport systems. These infrastructures (bicycle lanes) must comply the safety regulations, urban planning regulations and at the same time must be suitable for the practice of cycling [2], which is why their design has become an important task. for government agencies. From a scientific point of view, this design is possible thanks to the implementation of mathematical models that allow describing and analyzing real systems or situations by modeling the relationships between their elements. In this Final Degree Project, a methodology is proposed that allows solving the problem of designing a network of bike lanes considering the level of user satisfaction, as well as the implementation of multiple types of infrastructure, and it is illustrated your application using the network of the city of Seville, Spain.

En este trabajo se diseñará la red de líneas de autobús para la ciudad de Sevilla. Para ello, se tendrá un conjunto de líneas candidatas que, mediante la resolución de un método de optimización, se elegirán para obtener el diseño que reduzca el tiempo total y que cumpla el servicio deseado. El conjunto de paradas y calles por las que circularán los autobuses se representará mediante un grafo de nodos y arcos. En este trabajo, existe la particularidad que existirán varias capas o niveles de redes de autobús para la resolución por motivos que se especificarán en el trabajo., In the current research, a network of bus lines will be designed for the city of Seville. To achieve that, it will be given a group of lines considered as candidates. The design that minimizes the total time and achieve the desired service will be chosen through an optimization method. The set of bus stops and streets in which the buses will run will be represented as a graph of nodes and arcs. In this work, the graph representing the whole transportation system will contain several bus layers. The reason to select this network design will be explained later, when presenting the optimization model.

In this work, we address the Truck-multi-Drone Team Logistics Problem (TmDTL), devoted to visit a set of points with a truck helped by a team of unmanned aerial vehicles (UAVs) or drones in the minimum time, starting at a certain location and ending at a different one. It is an enhanced version of the multiple Flying Sidekicks Traveling Salesman Problem (mFSTSP) presented in Murray and Raj (2020) wherein drones are allowed to visit several customers per trip. In order to cope with large instances of the complex TmDTL, we have developed a novel agent-based method where agents represent the points that are going to be visited by vehicles. Agents evolve by means of movement inside a grid (locations vs. vehicles) according to a set of rules in the seek of better objective function values. Each agent needs to explore only a fraction of the complete problem, sharing its progress with the rest of the agents which are coordinated by one central agent which helps to maintain an asynchronous memory of solutions – e.g. on the control of the mechanism to escape from local minima. Our agent-based approach is firstly tested using the largest instances of the single TDTL problem reported in the literature, which additionally serves as upper bounds to the TmDTL problem. Secondly, we have solved instances up to 500 locations with up to 6 drones in the fleet. Thirdly, we have tested the behavior of our approach in 500 locations problems with up to 8 drones in order to test the fleet size sensitivity. Our experiments demonstrate the ability of the proposed agent-based system to obtain good quality solutions for complex optimization problems that arise. Further, the abstraction in solutions coding applied makes the agent-based approach scalable and flexible enough to be applied to a wide range of other optimization problems.

Electricity arbitrage involves the storage of energy at times when prices are low, and offering it on the markets when prices are high. The development of renewable and energy storage technologies may provide a promising business opportunity for electricity arbitrage. In this regard, this study analyses the current viability of the electricity arbitrage business (via Li-Ion batteries) using a sample of European countries in the year 2019; countries where electricity prices (day-ahead market) and financial conditions show a certain degree of heterogeneity. We basically follow a sequence of three analyses in our study. Firstly, a Linear Mixed-Integrated Programming model has been developed to optimize the arbitrage strategy for each country in the sample. Secondly, using the cash-flows from the optimization model, we calculate two financial indicators (Net Present Value (NPV) and Internal Rate of Return) in order to select the optimal inverter size for each country. Tax and discount rates specific to each country have been used with the calculation of this second rate following the methodology proposed by most of the national agencies. Thirdly, a mixed linear regression model is proposed in order to investigate the importance of observed and unobserved heterogeneity (at country level) in explaining the business profitability. The findings show that, in the near future, the most attractive European countries for the electricity arbitrage business should be the United Kingdom and Ireland, with current NPV close to −400,000 €, while Spain and Portugal might show the worst performances, their current NPV are close to −800,000 €.

In this work, we address the Truck-multi-Drone Team Logistics Problem (TmDTL), devoted to visit a set of points with a truck helped by a team of unmanned aerial vehicles (UAVs) or drones in the minimum time, starting at a certain location and ending at a different one. It is an enhanced version of the multiple Flying Sidekicks Traveling Salesman Problem (mFSTSP) presented in Murray and Raj (2020) wherein drones are allowed to visit several customers per trip. In order to cope with large instances of the complex TmDTL, we have developed a novel agent-based method where agents represent the points that are going to be visited by vehicles. Agents evolve by means of movement inside a grid (locations vs. vehicles) according to a set of rules in the seek of better objective function values. Each agent needs to explore only a fraction of the complete problem, sharing its progress with the rest of the agents which are coordinated by one central agent which helps to maintain an asynchronous memory of solutions – e.g. on the control of the mechanism to escape from local minima. Our agent-based approach is firstly tested using the largest instances of the single TDTL problem reported in the literature, which additionally serves as upper bounds to the TmDTL problem. Secondly, we have solved instances up to 500 locations with up to 6 drones in the fleet. Thirdly, we have tested the behavior of our approach in 500 locations problems with up to 8 drones in order to test the fleet size sensitivity. Our experiments demonstrate the ability of the proposed agent-based system to obtain good quality solutions for complex optimization problems that arise. Further, the abstraction in solutions coding applied makes the agent-based approach scalable and flexible enough to be applied to a wide range of other optimization problems.

We consider a generalization of the selective traveling salesman problem (STSP) in which the benefit of visiting a location changes over time. This new problem, called the selective travelling salesman problem with time-dependent profits (STSP-TDP), is defined on a graph with time-dependent profits associated with the vertices, and consists of determining a circuit of maximal total profit. In the STSP-TDP the tour length must not exceed a maximum value, and its starting and ending times must both lie within a prespecified planning horizon. This problem arises in planning tourist itineraries, mailbox collection, military surveillance, and water sampling, where the traveler accumulates different profits upon visiting the locations throughout the day. We focus on analyzing several variants of the problem depending on the shape of the time-dependent profit function. If this function is not monotonic, it may be worth visiting a site more than once. We propose formulations for the single-visit case and for when multiple visits are allowed, in which case the problem reduces to an STSP, which is adapted to be solved as a longest path problem. These formulations are then solved for piecewise-linear profit functions using a general-purpose solver, and tested on several artificially created instances and on four TSPLib instances involving up to 535 vertices. A detailed analysis of the problem and the solution is performed.

Electricity arbitrage involves the storage of energy at times when prices are low, and offering it on the markets when prices are high. The development of renewable and energy storage technologies may provide a promising business opportunity for electricity arbitrage. In this regard, this study analyses the current viability of the electricity arbitrage business (via Li-Ion batteries) using a sample of European countries in the year 2019; countries where electricity prices (day-ahead market) and financial conditions show a certain degree of heterogeneity. We basically follow a sequence of three analyses in our study. Firstly, a Linear Mixed-Integrated Programming model has been developed to optimize the arbitrage strategy for each country in the sample. Secondly, using the cash-flows from the optimization model, we calculate two financial indicators (Net Present Value (NPV) and Internal Rate of Return) in order to select the optimal inverter size for each country. Tax and discount rates specific to each country have been used with the calculation of this second rate following the methodology proposed by most of the national agencies. Thirdly, a mixed linear regression model is proposed in order to investigate the importance of observed and unobserved heterogeneity (at country level) in explaining the business profitability. The findings show that, in the near future, the most attractive European countries for the electricity arbitrage business should be the United Kingdom and Ireland, with current NPV close to −400,000 €, while Spain and Portugal might show the worst performances, their current NPV are close to −800,000 €.

The analysis of tandem truck–drone delivery systems has recently attracted the attention of the research community, mainly focused on extending classical operational research problems such as the multiple traveling salesperson or the vehicle-routing problem. In this paper, we explore the design of an urban massive combined delivery system using a continuum approximation (CA) method for a circular city characterized by a certain density of customers. Starting from a set of parameters defining the main characteristics of trucks and drones, a sectorization of the delivery area is first determined. Then, for a given truck capacity, the optimal number of trucks is obtained considering different scenarios using three integer programming models. We propose several performance indicators to compare the tandem approach with the alternative solely truck delivery system.

The analysis of tandem truck–drone delivery systems has recently attracted the attention of the research community, mainly focused on extending classical operational research problems such as the multiple traveling salesperson or the vehicle-routing problem. In this paper, we explore the design of an urban massive combined delivery system using a continuum approximation (CA) method for a circular city characterized by a certain density of customers. Starting from a set of parameters defining the main characteristics of trucks and drones, a sectorization of the delivery area is first determined. Then, for a given truck capacity, the optimal number of trucks is obtained considering different scenarios using three integer programming models. We propose several performance indicators to compare the tandem approach with the alternative solely truck delivery system.

El presente Trabajo Fin de Máster se centra en el estudio del problema de planificación de líneas de transporte público (TNDP) y en su resolución mediante un modelo de optimización que permita obtener el diseño óptimo de la red de transporte, minimizando el tiempo total de viaje en la red priorizando dar un servicio de calidad al usuario. Debido al gran tamaño que puede llegar a alcanzar el modelo de optimización para la planificación de líneas de una ciudad como Sevilla, este trabajo se focaliza en el desarrollo de métodos de generación de un pool de líneas candidatas como una etapa previa a la resolución del modelo, el cual seleccione un subconjunto de líneas candidatas factibles, que posteriormente serán introducidas como input al modelo, de forma que sea posible reducir el tamaño del modelo y, a su vez, obtener soluciones de buena calidad en términos de demanda atendida y tiempo total de viaje. Para ello, se desarrollan diferentes métodos, los cuales siguen distintos criterios para la selección de dicho subconjunto, y se lleva a cabo un análisis y comparativa de los resultados que cada uno de ellos permite obtener en el TNDP., This Master Thesis focuses on the study of the public transport line planning problem (TNDP) and its resolution by means of an optimization model that allows obtaining the optimal design of the transport network and prioritizing a quality service to the user. Due to the large size that can reach the optimization model for the planning of lines in a city like Seville, this work focuses on the development of methods for the generation of a pool of candidate lines as a previous stage to the generation of a pool of candidate line as a previous stage to the resolution of the model, which selects a subset of feasible candidate lines, which will later be introduced as input to the model, so that it is possible to reduce the size of the model and, in turn, obtain good quality solutions in terms of demand served and total travel time. For this purpose, different methods are developed, which follow different criteria for the selection of such subset, and an analysis and comparison of the results that each of them allows to obtain in the TNDP is carried out.

Electricity arbitrage involves the storage of energy at times when prices are low, and offering it on the markets when prices are high. The development of renewable and energy storage technologies may provide a promising business opportunity for electricity arbitrage. In this regard, this study analyses the current viability of the electricity arbitrage business (via Li-Ion batteries) using a sample of European countries in the year 2019; countries where electricity prices (day-ahead market) and financial conditions show a certain degree of heterogeneity. We basically follow a sequence of three analyses in our study. Firstly, a Linear Mixed-Integrated Programming model has been developed to optimize the arbitrage strategy for each country in the sample. Secondly, using the cash-flows from the optimization model, we calculate two financial indicators (Net Present Value (NPV) and Internal Rate of Return) in order to select the optimal inverter size for each country. Tax and discount rates specific to each country have been used with the calculation of this second rate following the methodology proposed by most of the national agencies. Thirdly, a mixed linear regression model is proposed in order to investigate the importance of observed and unobserved heterogeneity (at country level) in explaining the business profitability. The findings show that, in the near future, the most attractive European countries for the electricity arbitrage business should be the United Kingdom and Ireland, with current NPV close to −400,000 €, while Spain and Portugal might show the worst performances, their current NPV are close to −800,000 €.

In this work, we address the Truck-multi-Drone Team Logistics Problem (TmDTL), devoted to visit a set of points with a truck helped by a team of unmanned aerial vehicles (UAVs) or drones in the minimum time, starting at a certain location and ending at a different one. It is an enhanced version of the multiple Flying Sidekicks Traveling Salesman Problem (mFSTSP) presented in Murray and Raj (2020) wherein drones are allowed to visit several customers per trip. In order to cope with large instances of the complex TmDTL, we have developed a novel agent-based method where agents represent the points that are going to be visited by vehicles. Agents evolve by means of movement inside a grid (locations vs. vehicles) according to a set of rules in the seek of better objective function values. Each agent needs to explore only a fraction of the complete problem, sharing its progress with the rest of the agents which are coordinated by one central agent which helps to maintain an asynchronous memory of solutions – e.g. on the control of the mechanism to escape from local minima. Our agent-based approach is firstly tested using the largest instances of the single TDTL problem reported in the literature, which additionally serves as upper bounds to the TmDTL problem. Secondly, we have solved instances up to 500 locations with up to 6 drones in the fleet. Thirdly, we have tested the behavior of our approach in 500 locations problems with up to 8 drones in order to test the fleet size sensitivity. Our experiments demonstrate the ability of the proposed agent-based system to obtain good quality solutions for complex optimization problems that arise. Further, the abstraction in solutions coding applied makes the agent-based approach scalable and flexible enough to be applied to a wide range of other optimization problems.

In this paper, we analyze both the deterministic and stochastic versions of a team orienteering problem (TOP) in which rewards from customers are dynamic. The typical goal of the TOP is to select a set of customers to visit in order to maximize the total reward gathered by a fixed fleet of vehicles. To better reflect some real-life scenarios, we consider a version in which rewards associated with each customer might depend upon the order in which the customer is visited within a route, bonusing the first clients and penalizing the last ones. In addition, travel times are modeled as random variables. Two mixed-integer programming models are proposed for the deterministic version, which is then solved using a well-known commercial solver. Furthermore, a biased-randomized iterated local search algorithm is employed to solve this deterministic version. Overall, the proposed metaheuristic algorithm shows an outstanding performance when compared with the optimal or near-optimal solutions provided by the commercial solver, both in terms of solution quality as well as in computational times. Then, the metaheuristic algorithm is extended into a full simheuristic in order to solve the stochastic version of the problem. A series of numerical experiments allows us to show that the solutions provided by the simheuristic outperform the near-optimal solutions obtained for the deterministic version of the problem when the latter are used in a scenario under conditions of uncertainty. In addition, the solutions provided by our simheuristic algorithm for the stochastic version of the problem offer a higher reliability level than the ones obtained with the commercial solver.

We consider a generalization of the selective traveling salesman problem (STSP) in which the benefit of visiting a location changes over time. This new problem, called the selective travelling salesman problem with time-dependent profits (STSP-TDP), is defined on a graph with time-dependent profits associated with the vertices, and consists of determining a circuit of maximal total profit. In the STSP-TDP the tour length must not exceed a maximum value, and its starting and ending times must both lie within a prespecified planning horizon. This problem arises in planning tourist itineraries, mailbox collection, military surveillance, and water sampling, where the traveler accumulates different profits upon visiting the locations throughout the day. We focus on analyzing several variants of the problem depending on the shape of the time-dependent profit function. If this function is not monotonic, it may be worth visiting a site more than once. We propose formulations for the single-visit case and for when multiple visits are allowed, in which case the problem reduces to an STSP, which is adapted to be solved as a longest path problem. These formulations are then solved for piecewise-linear profit functions using a general-purpose solver, and tested on several artificially created instances and on four TSPLib instances involving up to 535 vertices. A detailed analysis of the problem and the solution is performed.

The analysis of tandem truck–drone delivery systems has recently attracted the attention of the research community, mainly focused on extending classical operational research problems such as the multiple traveling salesperson or the vehicle-routing problem. In this paper, we explore the design of an urban massive combined delivery system using a continuum approximation (CA) method for a circular city characterized by a certain density of customers. Starting from a set of parameters defining the main characteristics of trucks and drones, a sectorization of the delivery area is first determined. Then, for a given truck capacity, the optimal number of trucks is obtained considering different scenarios using three integer programming models. We propose several performance indicators to compare the tandem approach with the alternative solely truck delivery system.

In this work, we address the Truck-multi-Drone Team Logistics Problem (TmDTL), devoted to visit a set of points with a truck helped by a team of unmanned aerial vehicles (UAVs) or drones in the minimum time, starting at a certain location and ending at a different one. It is an enhanced version of the multiple Flying Sidekicks Traveling Salesman Problem (mFSTSP) presented in Murray and Raj (2020) wherein drones are allowed to visit several customers per trip. In order to cope with large instances of the complex TmDTL, we have developed a novel agent-based method where agents represent the points that are going to be visited by vehicles. Agents evolve by means of movement inside a grid (locations vs. vehicles) according to a set of rules in the seek of better objective function values. Each agent needs to explore only a fraction of the complete problem, sharing its progress with the rest of the agents which are coordinated by one central agent which helps to maintain an asynchronous memory of solutions – e.g. on the control of the mechanism to escape from local minima. Our agent-based approach is firstly tested using the largest instances of the single TDTL problem reported in the literature, which additionally serves as upper bounds to the TmDTL problem. Secondly, we have solved instances up to 500 locations with up to 6 drones in the fleet. Thirdly, we have tested the behavior of our approach in 500 locations problems with up to 8 drones in order to test the fleet size sensitivity. Our experiments demonstrate the ability of the proposed agent-based system to obtain good quality solutions for complex optimization problems that arise. Further, the abstraction in solutions coding applied makes the agent-based approach scalable and flexible enough to be applied to a wide range of other optimization problems.

In this paper, we analyze both the deterministic and stochastic versions of a team orienteering problem (TOP) in which rewards from customers are dynamic. The typical goal of the TOP is to select a set of customers to visit in order to maximize the total reward gathered by a fixed fleet of vehicles. To better reflect some real-life scenarios, we consider a version in which rewards associated with each customer might depend upon the order in which the customer is visited within a route, bonusing the first clients and penalizing the last ones. In addition, travel times are modeled as random variables. Two mixed-integer programming models are proposed for the deterministic version, which is then solved using a well-known commercial solver. Furthermore, a biased-randomized iterated local search algorithm is employed to solve this deterministic version. Overall, the proposed metaheuristic algorithm shows an outstanding performance when compared with the optimal or near-optimal solutions provided by the commercial solver, both in terms of solution quality as well as in computational times. Then, the metaheuristic algorithm is extended into a full simheuristic in order to solve the stochastic version of the problem. A series of numerical experiments allows us to show that the solutions provided by the simheuristic outperform the near-optimal solutions obtained for the deterministic version of the problem when the latter are used in a scenario under conditions of uncertainty. In addition, the solutions provided by our simheuristic algorithm for the stochastic version of the problem offer a higher reliability level than the ones obtained with the commercial solver.

We consider a generalization of the selective traveling salesman problem (STSP) in which the benefit of visiting a location changes over time. This new problem, called the selective travelling salesman problem with time-dependent profits (STSP-TDP), is defined on a graph with time-dependent profits associated with the vertices, and consists of determining a circuit of maximal total profit. In the STSP-TDP the tour length must not exceed a maximum value, and its starting and ending times must both lie within a prespecified planning horizon. This problem arises in planning tourist itineraries, mailbox collection, military surveillance, and water sampling, where the traveler accumulates different profits upon visiting the locations throughout the day. We focus on analyzing several variants of the problem depending on the shape of the time-dependent profit function. If this function is not monotonic, it may be worth visiting a site more than once. We propose formulations for the single-visit case and for when multiple visits are allowed, in which case the problem reduces to an STSP, which is adapted to be solved as a longest path problem. These formulations are then solved for piecewise-linear profit functions using a general-purpose solver, and tested on several artificially created instances and on four TSPLib instances involving up to 535 vertices. A detailed analysis of the problem and the solution is performed.

Electricity arbitrage involves the storage of energy at times when prices are low, and offering it on the markets when prices are high. The development of renewable and energy storage technologies may provide a promising business opportunity for electricity arbitrage. In this regard, this study analyses the current viability of the electricity arbitrage business (via Li-Ion batteries) using a sample of European countries in the year 2019; countries where electricity prices (day-ahead market) and financial conditions show a certain degree of heterogeneity. We basically follow a sequence of three analyses in our study. Firstly, a Linear Mixed-Integrated Programming model has been developed to optimize the arbitrage strategy for each country in the sample. Secondly, using the cash-flows from the optimization model, we calculate two financial indicators (Net Present Value (NPV) and Internal Rate of Return) in order to select the optimal inverter size for each country. Tax and discount rates specific to each country have been used with the calculation of this second rate following the methodology proposed by most of the national agencies. Thirdly, a mixed linear regression model is proposed in order to investigate the importance of observed and unobserved heterogeneity (at country level) in explaining the business profitability. The findings show that, in the near future, the most attractive European countries for the electricity arbitrage business should be the United Kingdom and Ireland, with current NPV close to −400,000 €, while Spain and Portugal might show the worst performances, their current NPV are close to −800,000 €.

La optimización de recursos a través de métodos matemáticos ha cobrado una gran importancia en los últimos años. Estas técnicas de optimización tienen numerosas y muy distintas aplicaciones. Entre otras, grandes empresas como Inditex basan toda su logística y organización en alguno de estos métodos. (ABC, 2018) En nuestro caso usaremos estas técnicas de optimización para tratar otro servicio cuya importancia debe aumentar en los próximos años: el transporte urbano y/o metropolitano por ferrocarril. En las próximas páginas se detallará un modelo matemático que aspira a dar solución, al menos parcialmente, al problema de la congestión en las estaciones de metro de grandes ciudades. En primer lugar, se realizará una introducción en la que se explica por qué el tren es un modo de transporte que no debería parar de crecer. Más adelante, se explicará el enfoque seguido y el método que se usará para paliar la congestión en las estaciones: la reserva de vagones. También se mostrarán los datos que resultan necesarios para nuestro caso de estudio, así como la explicación detrás de ellos. Para desarrollar el modelo propuesto, se han utilizado EXCEL, Python y GUROBI. Excel se utiliza tanto para recoger los datos como para plasmar los resultados. Python es el lenguaje en el que se han desarrollado las restricciones y la función objetivo del modelo. Por último, GUROBI es un programa de optimización lineal que nos ha permitido resolver el modelo, llevara a cabo los experimentos y estudiar distintas situaciones de una forma rápida, a pesar de manejar una gran cantidad de datos. Una vez seleccionados los datos de los diferentes experimentos, se resolverán varios escenarios para comprobar la efectividad del método. En concreto, comprobaremos cómo afecta al flujo de pasajeros la aplicación de políticas de reserva de plazas. Descifrar el comportamiento del modelo será clave para discernir su efectividad y concluir si realmente la reserva de vagones puede ser una solución adecuada para

COVID-19 se ha extendido alrededor del mundo causando un tremendo cambio estructural y afectando las cadenas de suministros globales y las operaciones financieras. Por ello, es de gran importancia implementar de manera correcta las medidas sanitarias, impuestas por las autoridades sanitarias, como el distanciamiento de seguridad o la limitación de aforos. La organización y planificación en los horarios de los transportes públicos, tales como las líneas de ferrocarril metropolitano, o metro, son muy complejas. Por ello, hay que implementar técnicas y utilizar herramientas potentes. En este estudio, a partir del trabajo de [20], se va a desarrollar un modelo de optimización para el diseño de horarios de líneas de metro con demanda variable que soporta estrategias Stop-skipping y Shortturning, cuyo propósito es el de disponer de un mayor número de servicios en los segmentos de mayor demanda de pasajeros, reduciendo el tiempo de espera en las estaciones. Además, se consideran zonas de operación, la capacidad de los trenes, las operaciones de cambio de sentido y el número de trenes disponibles. Para ello, se desarrolla una formulación no lineal, que posteriormente se resuelve utilizando la herramienta de optimización Gurobi Optimizer. El modelo se desarrolla mediante programación en lenguaje Python 3.8. Finalmente, se realizan varios experimentos, y se discuten los resultados., COVID-19 has spread around the world causing tremendous structural change and affecting global supply chains and financial operations. Therefore, it is of great importance to correctly implement sanitary measures, imposed by health authorities, such as safety distancing or capacity limitation. The organisation and planning of public transport schedules, such as metro and underground lines, are very complex. Therefore, it is necessary to implement techniques and use powerful tools. In this study, based on the work of [20], an optimisation model is developed for the design of metro line timetables with variable demand that supports stop-skipping and short-turning strategies, the purpose of which is to provide a greater number of services in the segments with the highest passenger demand, reducing waiting time at stations. In addition, operating zones, train capacity, change of direction operations and the number of available trains are considered. For this purpose, a non-linear formulation is developed, which is subsequently solved using the optimisation tool Gurobi Optimizer. The model is developed using Python 3.8 programming language. Finally, several experiments are performed, and the results are discussed.

En la sociedad actual, resulta imprescindible la utilización de los medios de transporte. En los grandes núcleos urbanos, una gran cantidad de pasajeros se desplazan diariamente desde sus domicilios u otros orígenes, a unos ciertos destinos, para poder llevar a cabo sus actividades cotidianas. Sin embargo, el diseño y operación de los medios de transporte exige la realización de una serie de estudios para poder llevar a cabo desde su construcción y planificación, al diseño de horarios de desplazamientos, implicando una gran cantidad de variables y datos. Concretamente, en este estudio se trata el diseño del horario de una línea de red de metro en la que se debe satisfacer una demanda de pasajeros variable con el tiempo, en las distintas plataformas de la línea. Para la determinación del diseño óptimo, se formula una estrategia skip-stopping, que permite que los servicios no se detengan en algunas plataformas de la línea. Dada la complejidad ofrecida por este problema de optimización, en el ámbito que les compete, los ingenieros, se apoyan en herramientas informáticas para su proyección y resolución matemática., In today's society, the use of transport is essential. In large urban centres, a large number of passengers travel daily from their homes or other origins to certain destinations in order to carry out their daily activities. However, the design and operation of means of transport requires a series of studies to be carried out, from construction and planning to the design of travel schedules, involving a large number of variables and data. Specifically, this study deals with the design of the timeTabla of a metro network line in which a time-varying passenger demand must be satisfied on the different platforms of the line. In order to determine the optimal design, a skip-stopping strategy is formulated, which allows services not to stop at some platforms of the line. Given the complexity of this optimisation problem, the engineers rely on computer tools for its projection and mathematical resolution.

En el sector sanitario el papel de los hospitales es principal, por lo que una gestión eficiente implica el avance hacia la mejora de prestaciones para la población. A la hora de valorar el desempeño general de cada hospital, surgen dudas como: ¿qué factores determinan la eficiencia o ineficiencia? ¿cuáles son los hospitales eficientes? ¿qué áreas dentro del hospital son las que provocan la ineficiencia? ¿qué podemos hacer para mejorarlas? Con las respuestas a estas preguntas, los encargados de diseñar las políticas sanitarias pueden establecer criterios y elaborar procedimientos para mejorar los resultados. (extraído del primer capítulo)

A lo largo de este Trabajo Fin de Master se desarrollan una serie de pautas para la implementación real de las principales técnicas Lean Manufacturing con el objetivo de mejorar la producción. Desde su orígenes a mediados de los 70 en Japón, las herramientas Lean Manufacturing han ido evolucionando y adaptándose a todos los sectores industriales. Evidenciándose de forma clara cómo el uso de las mismas conlleva una mejor organización en el ambiente productivo y una reducción de despilfarros. Ocasionando por tanto, una disminución de los costes y un aumento de los beneficios empresariales. Las técnicas Lean Manufacturing son parte del ámbito ingenieril, dentro de la organización industrial, pudiendo ser aplicadas a cualquier sector productivo. A lo largo del presente proyecto se describen las principales técnicas Lean Manufacturing utilizadas actualmente, para posteriormente presentar un modelo de implementación en una planta de fabricación de componentes aeronáuticos de materiales compuestos (carbono/epoxy, kevlar, fibra de vidrio). La industria aeronáutica siempre se ha caracterizado por ser pionera en innovación y desarrollo, debido al gran volumen de inversión y estrategia que engloba este negocio. Además, con el progreso de los diseños y las estructuras avionables cada vez más ligeras y eficientes, poco a poco el uso de materiales compuestos a bordo está cobrando un mayor peso en todos los diseños estructurales. Este Trabajo Fin de Master es el resultado de haber formado parte durante 7 meses de la empresa Carbures, y tras observar los problemas que se presentan en la misma, estudiar las posibles soluciones aplicando la metodología Lean, y con el fin de mejorar la producción. Para ello, se propone un plan de implementación de algunas herramientas Lean en las áreas principales dentro de la planta de fabricación. Dicho plan no ha sido aún puesto en práctica, pero se espera que en el futuro sirva de ayuda para el avance del Lean Manufacturing en la factoría. Por últ, Throughout this Master’s Thesis, a series of guidelines are developed for a real implementation of the main Lean Manufacturing techniques with the aim of improving production. Since its origins in the mid-70s in Japan, Lean Manufacturing tools have been evolving and adapting to all industrial sectors. It has become clear how their use leads to a better organization in the production environment and a reduction in waste. Therefore, a decrease in costs and an increase in business profits. Lean Manufacturing techniques are part of the engineering field, within the industrial organization. They can be applied to any productive sector. During the present project, the main Lean Manufacturing techniques currently used are described, to later present a model of implementation in a plant manufacturing aeronautical components of composite materials (carbon/epoxy, kevlar, fibreglass). The aeronautical industry has always been characterised by its pioneering role in innovation and development, due to the large volume of investment and strategy that this business encompasses. In addition, with the progress of designs and airplane structures that are increasingly lighter and more efficient, the use of composite materials on board is gradually gaining more weight in all structural designs. This Final Project is the result of having been part of Carbures for 7 months, and after observing the problems that arise in the company, studying possible solutions applying the Lean methodology with the aim of improving production. For this purpose, a plan is proposed to implement some Lean tools in the main areas within the manufacturing plant. This plan has not yet been put into practice, but it is hoped that in the future it will help to advance Lean Manufacturing in the factory. Finally, this project is completed with the presentation of the conclusions drawn from it, as well as the possible areas for improvement.

A lo largo de este Trabajo Fin de Master se desarrollan una serie de pautas para la implementación real de las principales técnicas Lean Manufacturing con el objetivo de mejorar la producción. Desde su orígenes a mediados de los 70 en Japón, las herramientas Lean Manufacturing han ido evolucionando y adaptándose a todos los sectores industriales. Evidenciándose de forma clara cómo el uso de las mismas conlleva una mejor organización en el ambiente productivo y una reducción de despilfarros. Ocasionando por tanto, una disminución de los costes y un aumento de los beneficios empresariales. Las técnicas Lean Manufacturing son parte del ámbito ingenieril, dentro de la organización industrial, pudiendo ser aplicadas a cualquier sector productivo. A lo largo del presente proyecto se describen las principales técnicas Lean Manufacturing utilizadas actualmente, para posteriormente presentar un modelo de implementación en una planta de fabricación de componentes aeronáuticos de materiales compuestos (carbono/epoxy, kevlar, fibra de vidrio). La industria aeronáutica siempre se ha caracterizado por ser pionera en innovación y desarrollo, debido al gran volumen de inversión y estrategia que engloba este negocio. Además, con el progreso de los diseños y las estructuras avionables cada vez más ligeras y eficientes, poco a poco el uso de materiales compuestos a bordo está cobrando un mayor peso en todos los diseños estructurales. Este Trabajo Fin de Master es el resultado de haber formado parte durante 7 meses de la empresa Carbures, y tras observar los problemas que se presentan en la misma, estudiar las posibles soluciones aplicando la metodología Lean, y con el fin de mejorar la producción. Para ello, se propone un plan de implementación de algunas herramientas Lean en las áreas principales dentro de la planta de fabricación. Dicho plan no ha sido aún puesto en práctica, pero se espera que en el futuro sirva de ayuda para el avance del Lean Manufacturing en la factoría. Por últ, Throughout this Master’s Thesis, a series of guidelines are developed for a real implementation of the main Lean Manufacturing techniques with the aim of improving production. Since its origins in the mid-70s in Japan, Lean Manufacturing tools have been evolving and adapting to all industrial sectors. It has become clear how their use leads to a better organization in the production environment and a reduction in waste. Therefore, a decrease in costs and an increase in business profits. Lean Manufacturing techniques are part of the engineering field, within the industrial organization. They can be applied to any productive sector. During the present project, the main Lean Manufacturing techniques currently used are described, to later present a model of implementation in a plant manufacturing aeronautical components of composite materials (carbon/epoxy, kevlar, fibreglass). The aeronautical industry has always been characterised by its pioneering role in innovation and development, due to the large volume of investment and strategy that this business encompasses. In addition, with the progress of designs and airplane structures that are increasingly lighter and more efficient, the use of composite materials on board is gradually gaining more weight in all structural designs. This Final Project is the result of having been part of Carbures for 7 months, and after observing the problems that arise in the company, studying possible solutions applying the Lean methodology with the aim of improving production. For this purpose, a plan is proposed to implement some Lean tools in the main areas within the manufacturing plant. This plan has not yet been put into practice, but it is hoped that in the future it will help to advance Lean Manufacturing in the factory. Finally, this project is completed with the presentation of the conclusions drawn from it, as well as the possible areas for improvement.

The problem of managing selective collection of waste using containers inside historic city centres can be performed in three sequential phases: first, locating containers along the streets; then, determining the minimum fleet size required to perform all collecting services; and finally, identifying the optimal collection routes. Obviously, the result of the first phase highly influences the procedure since this will determine the decisions to be taken for the subsequent phases. This paper is focused on the first phase. Facility-customer distances, the size of container groups and the cost of installing those containers in specific sites along the streets are parameters to be considered when designing the collecting system. Additionally, we assume that customers are willing to have a solidarity behaviour, which consists of using a container assigned to them within a pre-established proximity radius, although the assigned container may not be necessarily the closest to their residence. For this scenario, a more efficient deployment of containers for selective collection of urban solid waste can be obtained. To illustrate the performance of the developed methodology, a computational experience has been carried out on a network with randomized data based on a zone belonging to city of Seville (Spain).

El presente trabajo fin de master se enmarca en el campo de la programación y secuenciación de la producción aplicado a un contexto industrial real. Se trabaja con una organización dedicada a la fabricación de envases de plástico para el sector alimentario. El producto en el que nos vamos a centrar es una pieza de plástico que se procesa en unas máquinas, en adelante líneas, en las que entra el plástico fundido y, a través de su inyección en un molde y de un ensamblaje final se obtiene el producto terminado. Estas líneas están automatizadas, pero cuando hay que cambiar entre los diferentes colores del producto a fabricar se requiere de un proceso de preparación de las líneas o setup que supone importantes costes para la empresa. Se conocen las referencias a producir a través de pedidos de aprovisionamiento del stock del almacén o pedidos que se reciben directamente de clientes. En ambos casos se dispone de un plazo de al menos 15 días para la entrega a clientes. A todo esto, hay que sumarle las necesarias paradas para el mantenimiento preventivo de las líneas, que deben ser contempladas en la programación de la producción. El sistema de producción de la compañía se compone de un conjunto de líneas de fabricación, dispuestas en paralelo, con distintas velocidades y con puntuales paradas para un mantenimiento preventivo. Estas líneas deben procesar unos pedidos sujetos a unas fechas de entrega. Los cambios entre las referencias de cada pedido en una línea provocan la aparición de unos tiempos de setup que se desean minimizar. Para resolver este problema se propone un método de resolución exacta mediante la aplicación sucesiva de dos modelos de optimización. En una primera fase se pretende simplificar el tamaño del problema mediante la preasignación de los pedidos a subconjuntos de líneas de fabricación en función del color de los pedidos. En la segunda fase se resuelve el modelo de programación de la producción teniendo en cuenta las restricciones y condicionantes men, This final master's work is part of the field of production scheduling and sequencing applied to a real industrial context. We work with an organization dedicated to the manufacture of plastic containers for the food sector. The product we are going to focus on is a piece of plastic that is processed in machines, hereinafter lines, in which the molten plastic enters and, through its injection into a mold and a final assembly, the finished product. These lines are automated, but when it is necessary to change between the different colors of the product to be manufactured, a line preparation or setup process is required, which entails significant costs for the company. The references to be produced are known through supply orders for warehouse stock or orders received directly from customers. In both cases there is a period of at least 15 days for delivery to customers. To all this, we must add the necessary stops for the preventive maintenance of the lines, which must be considered in the production scheduling. The company's production system is made up of a set of manufacturing lines, arranged in parallel, with different speeds and with occasional stops for preventive maintenance. These lines must process orders subject to delivery dates. Changes between the references of each order in a line cause the appearance of setup times that you want to minimize. To solve this problem, an exact resolution method is proposed through the successive application of two optimization models. In a first phase, the aim is to simplify the size of the problem by pre-assigning orders to subsets of production lines based on the color of the orders. In the second phase, the production scheduling model is solved taking into account the aforementioned restrictions and conditions.

En el sector sanitario el papel de los hospitales es principal, por lo que una gestión eficiente implica el avance hacia la mejora de prestaciones para la población. A la hora de valorar el desempeño general de cada hospital, surgen dudas como: ¿qué factores determinan la eficiencia o ineficiencia? ¿cuáles son los hospitales eficientes? ¿qué áreas dentro del hospital son las que provocan la ineficiencia? ¿qué podemos hacer para mejorarlas? Con las respuestas a estas preguntas, los encargados de diseñar las políticas sanitarias pueden establecer criterios y elaborar procedimientos para mejorar los resultados. (extraído del primer capítulo)

The problem of managing selective collection of waste using containers inside historic city centres can be performed in three sequential phases: first, locating containers along the streets; then, determining the minimum fleet size required to perform all collecting services; and finally, identifying the optimal collection routes. Obviously, the result of the first phase highly influences the procedure since this will determine the decisions to be taken for the subsequent phases. This paper is focused on the first phase. Facility-customer distances, the size of container groups and the cost of installing those containers in specific sites along the streets are parameters to be considered when designing the collecting system. Additionally, we assume that customers are willing to have a solidarity behaviour, which consists of using a container assigned to them within a pre-established proximity radius, although the assigned container may not be necessarily the closest to their residence. For this scenario, a more efficient deployment of containers for selective collection of urban solid waste can be obtained. To illustrate the performance of the developed methodology, a computational experience has been carried out on a network with randomized data based on a zone belonging to city of Seville (Spain).

The problem of managing selective collection of waste using containers inside historic city centres can be performed in three sequential phases: first, locating containers along the streets; then, determining the minimum fleet size required to perform all collecting services; and finally, identifying the optimal collection routes. Obviously, the result of the first phase highly influences the procedure since this will determine the decisions to be taken for the subsequent phases. This paper is focused on the first phase. Facility-customer distances, the size of container groups and the cost of installing those containers in specific sites along the streets are parameters to be considered when designing the collecting system. Additionally, we assume that customers are willing to have a solidarity behaviour, which consists of using a container assigned to them within a pre-established proximity radius, although the assigned container may not be necessarily the closest to their residence. For this scenario, a more efficient deployment of containers for selective collection of urban solid waste can be obtained. To illustrate the performance of the developed methodology, a computational experience has been carried out on a network with randomized data based on a zone belonging to city of Seville (Spain).

The problem of managing selective collection of waste using containers inside historic city centres can be performed in three sequential phases: first, locating containers along the streets; then, determining the minimum fleet size required to perform all collecting services; and finally, identifying the optimal collection routes. Obviously, the result of the first phase highly influences the procedure since this will determine the decisions to be taken for the subsequent phases. This paper is focused on the first phase. Facility-customer distances, the size of container groups and the cost of installing those containers in specific sites along the streets are parameters to be considered when designing the collecting system. Additionally, we assume that customers are willing to have a solidarity behaviour, which consists of using a container assigned to them within a pre-established proximity radius, although the assigned container may not be necessarily the closest to their residence. For this scenario, a more efficient deployment of containers for selective collection of urban solid waste can be obtained. To illustrate the performance of the developed methodology, a computational experience has been carried out on a network with randomized data based on a zone belonging to city of Seville (Spain).

El rápido desarrollo en los últimos años de las tecnologías de vehículos aéreos no tripulados (UAV), conocidos popularmente como drones, está produciendo un aumento notable en el número de campos de aplicación. En la actualidad estos vehículos desempeñan, entre otras muchas, diversas tareas. (Extraído de la Introducción)

En la actualidad, es cada vez más común que las empresas recurran a algún softwareque les permita gestionar las relaciones con sus clientes, esto eslo que se conoce como CRM. Para ello surgen numerosas herramientas, como la plataforma Salesforce, en la que se va a centrar este documento, por ser una de las más demandadas en el mercado hoy en día.Al ser una herramienta compleja y con numerosas posibilidades, las empresas suelen recurrir a equipos especializados que analicen las necesidades, configuren la plataforma y den una solución a éstas, de la manera más eficiente. Este documento se centrará en el papel de estos equipos, y decómo debe ser la gestión de un proyecto de estetipo.En concreto se analizarálagestión deun proyecto de Salesforcesiguiendo una metodologíaágil, basada en una mayor interacción entre los participantesy unanálisisy revisión continuos dela solución que se vaconstruyendo.Este análisis sehará tantodesde un punto de vista teórico,revisando las característicasy herramientasque se emplean en la gestión ágil,cómo desde unpuntovista práctico, donde serevisarála aplicación de estas herramientasparael caso concreto de un proyecto en Salesforce., Nowadays, it is increasingly common for companies to use some software that allows them to manage relationships with their customers, this is what is known as CRM. For this reason, numerous tools have appeared, such as the Salesforce platform, on which this document will focus, because it is one of the most demanded in the market today.Because it is a complex tool with many possibilities, companies often hire specialized teams to analyze the needs, configure the platform and provide a solution to them, in the most efficient way. This document will focuson the role of these teams, and how a project of this type should be managed.Specifically, the management of a Salesforce project following an agile methodology will be analyzed. This agile methodology is based on a greater interaction between the participants and a continuous analysis and review of the solution that is being built. This analysis will be done both from a theoretical point of view, reviewing the characteristics and tools used in agile management, and from a practical point of view, where the application of these tools will be reviewed for the specific case of a project in Salesforce.

The problem of managing selective collection of waste using containers inside historic city centres can be performed in three sequential phases: first, locating containers along the streets; then, determining the minimum fleet size required to perform all collecting services; and finally, identifying the optimal collection routes. Obviously, the result of the first phase highly influences the procedure since this will determine the decisions to be taken for the subsequent phases. This paper is focused on the first phase. Facility-customer distances, the size of container groups and the cost of installing those containers in specific sites along the streets are parameters to be considered when designing the collecting system. Additionally, we assume that customers are willing to have a solidarity behaviour, which consists of using a container assigned to them within a pre-established proximity radius, although the assigned container may not be necessarily the closest to their residence. For this scenario, a more efficient deployment of containers for selective collection of urban solid waste can be obtained. To illustrate the performance of the developed methodology, a computational experience has been carried out on a network with randomized data based on a zone belonging to city of Seville (Spain).

The problem of managing selective collection of waste using containers inside historic city centres can be performed in three sequential phases: first, locating containers along the streets; then, determining the minimum fleet size required to perform all collecting services; and finally, identifying the optimal collection routes. Obviously, the result of the first phase highly influences the procedure since this will determine the decisions to be taken for the subsequent phases. This paper is focused on the first phase. Facility-customer distances, the size of container groups and the cost of installing those containers in specific sites along the streets are parameters to be considered when designing the collecting system. Additionally, we assume that customers are willing to have a solidarity behaviour, which consists of using a container assigned to them within a pre-established proximity radius, although the assigned container may not be necessarily the closest to their residence. For this scenario, a more efficient deployment of containers for selective collection of urban solid waste can be obtained. To illustrate the performance of the developed methodology, a computational experience has been carried out on a network with randomized data based on a zone belonging to city of Seville (Spain).

The problem of managing selective collection of waste using containers inside historic city centres can be performed in three sequential phases: first, locating containers along the streets; then, determining the minimum fleet size required to perform all collecting services; and finally, identifying the optimal collection routes. Obviously, the result of the first phase highly influences the procedure since this will determine the decisions to be taken for the subsequent phases. This paper is focused on the first phase. Facility-customer distances, the size of container groups and the cost of installing those containers in specific sites along the streets are parameters to be considered when designing the collecting system. Additionally, we assume that customers are willing to have a solidarity behaviour, which consists of using a container assigned to them within a pre-established proximity radius, although the assigned container may not be necessarily the closest to their residence. For this scenario, a more efficient deployment of containers for selective collection of urban solid waste can be obtained. To illustrate the performance of the developed methodology, a computational experience has been carried out on a network with randomized data based on a zone belonging to city of Seville (Spain).

In recent years, the growth of fossil fuel use and greenhouse gases emissions (GHGs) has been promoted by the population increase and development of the industry sector. Due to the increasing attention towards the effects of climate changes on quality of life, recent researches on pollutant formation processes have been developed in different sectors, especially in transportation. The last emission standards on pollutants impose limits on the dimensions and on the particle number of the particulate matter emissions, because of the highly dangerous effect on human health. To fight high concentrations of particulate matter (PM) emissions, a wide number of studies are addressed towards the definition of the most important parameters in effective production of particulate matter, especially in spark ignition engines. Physical processes such as mixture formation, engine operating parameters and fuel chemical properties strongly affect the soot formation in gasoline engines. The heat transfer process between the piston hot surface and the fuel gasoline during the post-injection phase is a key aspect of soot emissions for an engine. This paper is devoted to analyzing the fundamental parameters that are responsible for pollutant formation in the transport sector and the actual experimental and numerical techniques used to predict the environmental impact of engines.

Este proyecto aborda la planificación del calendario de entrenamientos de una escuela o cantera de fútbol base de modo que se optimicen tanto los resultados deportivos de los diferentes equipos y deportistas como el aprovechamiento de las instalaciones de las que disponga el club específico. El proyecto se encuentra desarrollado en dos partes fácilmente diferenciables. La primera es una etapa teórica en la cual se hace una descripción genérica, en la medida de lo posible, del problema y se desarrollan los modelos de programación lineal a través de los cuales se resolverá un problema concreto en la segunda etapa. Este problema es la programación de los entrenamientos de los equipos que componen la cantera del Sevilla Fútbol Club.

El objeto de este Trabajo de Fin de Máster es analizar económicamente la viabilidad del almacenamiento de energía en baterías de litio con los resultados obtenidos de un modelo matemático. Para poder abordar este problema previamente se ha llevado a cabo un análisis de la literatura en el que se ha investigado sobre la situación actual de las diferentes tecnologías de almacenamiento de energía con las características y los principales usos que tiene cada una. Gracias al estado del arte se ha detallado cómo es el funcionamiento del sistema eléctrico español explicando los diferentes tipos de energía que existen tanto renovable como no renovable. También se exponen los mercados eléctricos en los que se llevan a cabo las transacciones de energía eléctrica para obtener los precios de la electricidad para cada hora del día. Estos precios se utilizarán para conseguir los resultados en los capítulos del modelo matemático y del análisis financiero. Consecutivamente se ha realizado una descripción de las diferentes baterías que existen en el mercado y el motivo por el que se decide ejecutar el trabajo con una batería de litio. Utilizando el modelo matemático que el profesor David Canca Ortiz ha creado para otras investigaciones paralelas, se consigue averiguar la cantidad de energía que habría que comprar o vender en cada hora del año durante el tiempo en el que dure la batería según las características impuestas en el modelo. A partir de los resultados obtenidos del modelo matemático utilizado, se trata de identificar mediante un análisis financiero qué tamaño de inversor será el óptimo con el tamaño de batería empleado y, de esta forma, tomar una serie de decisiones al respecto., The purpose of this Master’s Degree Final Project is to analyse economically the viability of storing energy in lithium ion batteries with the results obtained from a mathematical model. Previously, a detailed analysis of the literature has been carried out in which the current situation of different energy storage technologies has been investigated with the features and the main uses they have. Thanks to the state of art, it has been described how the Spanish electric system works explaining the existing different types of energy, both renewable and non-renewable. Also, the electricity markets where the electricity agreements take place to acquire the hourly price of the electricity, have been explained. These prices will be used to achieve the results in the mathematical model and financial analysis chapters. Consecutively, the different existing batteries have been described and the reason why the lithium ion battery was chosen. Using the mathematical model created by professor David Canca Ortiz, it might be possible to calculate the amount of energy to buy and sell at every hour of the year during the battery’s life, knowing the features imposed in the model. Based on the results obtained from the mathematical model used, the aim is to identify through a financial analysis which electric inverter size will be optimal with the battery size used and, in this way, take measures in that regard.

In recent years, the growth of fossil fuel use and greenhouse gases emissions (GHGs) has been promoted by the population increase and development of the industry sector. Due to the increasing attention towards the effects of climate changes on quality of life, recent researches on pollutant formation processes have been developed in different sectors, especially in transportation. The last emission standards on pollutants impose limits on the dimensions and on the particle number of the particulate matter emissions, because of the highly dangerous effect on human health. To fight high concentrations of particulate matter (PM) emissions, a wide number of studies are addressed towards the definition of the most important parameters in effective production of particulate matter, especially in spark ignition engines. Physical processes such as mixture formation, engine operating parameters and fuel chemical properties strongly affect the soot formation in gasoline engines. The heat transfer process between the piston hot surface and the fuel gasoline during the post-injection phase is a key aspect of soot emissions for an engine. This paper is devoted to analyzing the fundamental parameters that are responsible for pollutant formation in the transport sector and the actual experimental and numerical techniques used to predict the environmental impact of engines.

In recent years, the growth of fossil fuel use and greenhouse gases emissions (GHGs) has been promoted by the population increase and development of the industry sector. Due to the increasing attention towards the effects of climate changes on quality of life, recent researches on pollutant formation processes have been developed in different sectors, especially in transportation. The last emission standards on pollutants impose limits on the dimensions and on the particle number of the particulate matter emissions, because of the highly dangerous effect on human health. To fight high concentrations of particulate matter (PM) emissions, a wide number of studies are addressed towards the definition of the most important parameters in effective production of particulate matter, especially in spark ignition engines. Physical processes such as mixture formation, engine operating parameters and fuel chemical properties strongly affect the soot formation in gasoline engines. The heat transfer process between the piston hot surface and the fuel gasoline during the post-injection phase is a key aspect of soot emissions for an engine. This paper is devoted to analyzing the fundamental parameters that are responsible for pollutant formation in the transport sector and the actual experimental and numerical techniques used to predict the environmental impact of engines.

In recent years, the growth of fossil fuel use and greenhouse gases emissions (GHGs) has been promoted by the population increase and development of the industry sector. Due to the increasing attention towards the effects of climate changes on quality of life, recent researches on pollutant formation processes have been developed in different sectors, especially in transportation. The last emission standards on pollutants impose limits on the dimensions and on the particle number of the particulate matter emissions, because of the highly dangerous effect on human health. To fight high concentrations of particulate matter (PM) emissions, a wide number of studies are addressed towards the definition of the most important parameters in effective production of particulate matter, especially in spark ignition engines. Physical processes such as mixture formation, engine operating parameters and fuel chemical properties strongly affect the soot formation in gasoline engines. The heat transfer process between the piston hot surface and the fuel gasoline during the post-injection phase is a key aspect of soot emissions for an engine. This paper is devoted to analyzing the fundamental parameters that are responsible for pollutant formation in the transport sector and the actual experimental and numerical techniques used to predict the environmental impact of engines.

Durante las últimas décadas, la necesidad de movilidad de las personas ha sufrido un increíble aumento, lo que ha provocado una gran demanda diaria de los medios de transporte público, y como consecuencia, del transporte ferroviario. Además, tal demanda sigue aumentando día a día en número y en exigencia, por lo que resulta fundamental lograr una planificación adecuada del servicio. El presente proyecto, se centra en una de las etapas necesarias para su consecución, concretamente, tiene el propósito de desarrollar e implementar un modelo de programación lineal mixta-entera que permita determinar las frecuencias óptimas junto con el resto de variables necesarias para programar cada una de las líneas que componen una red de cercanías, atendiendo a las características y exigencias que supone un sistema de transporte como el que se presenta. Adicionalmente, debido a que el modelo se propone para una red compuesta por diferentes líneas que cuentan con varias estaciones en común, los pasajeros tendrán en la mayoría de las ocasiones diferentes opciones o rutas para llegar a un mismo destino, y por ello, surge la necesidad de realizar una asignación de tránsito que nos permita ver cómo se comportan los usuarios dada la red de transporte desarrollada, es decir, que estrategias de viaje seleccionan para llegar al destino deseado. Los dos problemas presentados guardan una estrecha relación, ya que, en base a las frecuencias establecidas, los pasajeros decidirán escoger una ruta u otra, pero al mismo tiempo, en función del número de viajeros que utilicen una línea, se establecerá una determinada frecuencia de paso. Por lo tanto, se busca alcanzar un equilibrio entre ambos problemas. Así mismo, con el objetivo de satisfacer al mismo tiempo tanto a operadores como a pasajeros, se persigue en todo momento la minimización de los gastos de tripulación y de los costes de operación de los diferentes modelos de tren puestos en marcha, y, junto con ello, minimizar también el valor monet, Over the last decades, the people´s need for mobility has suffered an incredible increase, which has caused a great daily demand for public transport, and consequently, for rail transport. In addition, such demand continues to increase day by day in number and in request, so it is essential to achieve an adequate planning of the service. The present project focuses on one of the necessary stages for its achievement, specifically, it has the purpose of developing a linear programming model that allows determining the optimal frequencies along with the rest of necessary variables to schedule each of the lines that make up a commuter rail network, considering the characteristics and requests of a transport system such as the one presented. Additionally, due to the model is proposed for a network composed of different lines that have in common several stations, in most cases, passengers will have different options or routes to reach the same destination and, because of that, the need arises to make a transit assignment that allows us to see how the users behave given the transport network developed, that is, which travel strategies they select to reach the desired destination. The two problems presented are closely related, since, based on the established frequencies, passengers will choose one route or another, but at the same time, depending on the number of passengers using a line, a certain frequency will be established. Therefore, it is sought to find a balance between both problems. Likewise, with the objective of satisfying both operators and passengers at the same time, it is pursued the minimization of crew costs and operation costs of the different train models put in place, and, along with this, also minimize the monetary value of the total trip time used by passengers to reach their corresponding destinations, including not only on-board travel times, but also transfer times and waiting times. However, since both objectives are opposed, before choosing a fin

Considerando la situación a la cual ha llegado la contaminación atmosférica y la congestión vehicular en las grandes ciudades, se han impulsado medidas para promover modos de transporte sostenible, como las bicicletas públicas, buscando una alternativa a la utilización de vehículos motorizados. Los sistemas públicos de bicicletas han incrementado su popularidad como sistemas de transporte sostenibles en los últimos años en muchos países del mundo. Dentro de los elementos más importantes para su implementación, se encuentra la localización óptima de las estaciones de recogida y devolución de bicicletas. En este estudio se desarrollan dos modelos de localización-asignación para resolver el problema de diseño de una red de bicicletas públicas. Se logra determinar el número de estaciones que son necesarias, la ubicación de las estaciones y la flota total de bicicletas del sistema, considerando restricciones de presupuesto. En un caso maximizando la demanda y en el otro priorizando la selección de la capacidad de las estaciones según la demanda. Los modelos se plantean para la cuidad de Montevideo, Uruguay y se resuelven computacionalmente utilizado lenguaje de programación Python y software de optimización Gurobi®., Considering the global situation related to air pollution and vehicular congestion, many countries are promoting sustainable modes of transport, as an alternative to motorized vehicles, such as bike-sharing . Bike-sharing systems are increasing their popularity as sustainable transportation systems in recent years. One of the most important elements for its implementation is the optimal location of the stations. This work presents two different location-allocation models to solve the design problem of the bike-sharing system. The models determines the require number of stations, the location of the stations and the total fleet of bicycles in the system, considering budget constraints. In one case the objective function is to maximize the demand and in the other, the suitable selection of different types of stations according to demand. The models are applied to Montevideo, Uruguay and are computationally solved using Python programming language and Gurobi® optimization software.

Considerando la situación a la cual ha llegado la contaminación atmosférica y la congestión vehicular en las grandes ciudades, se han impulsado medidas para promover modos de transporte sostenible, como las bicicletas públicas, buscando una alternativa a la utilización de vehículos motorizados. Los sistemas públicos de bicicletas han incrementado su popularidad como sistemas de transporte sostenibles en los últimos años en muchos países del mundo. Dentro de los elementos más importantes para su implementación, se encuentra la localización óptima de las estaciones de recogida y devolución de bicicletas. En este estudio se desarrollan dos modelos de localización-asignación para resolver el problema de diseño de una red de bicicletas públicas. Se logra determinar el número de estaciones que son necesarias, la ubicación de las estaciones y la flota total de bicicletas del sistema, considerando restricciones de presupuesto. En un caso maximizando la demanda y en el otro priorizando la selección de la capacidad de las estaciones según la demanda. Los modelos se plantean para la cuidad de Montevideo, Uruguay y se resuelven computacionalmente utilizado lenguaje de programación Python y software de optimización Gurobi®., Considering the global situation related to air pollution and vehicular congestion, many countries are promoting sustainable modes of transport, as an alternative to motorized vehicles, such as bike-sharing . Bike-sharing systems are increasing their popularity as sustainable transportation systems in recent years. One of the most important elements for its implementation is the optimal location of the stations. This work presents two different location-allocation models to solve the design problem of the bike-sharing system. The models determines the require number of stations, the location of the stations and the total fleet of bicycles in the system, considering budget constraints. In one case the objective function is to maximize the demand and in the other, the suitable selection of different types of stations according to demand. The models are applied to Montevideo, Uruguay and are computationally solved using Python programming language and Gurobi® optimization software.

Durante las últimas décadas, la necesidad de movilidad de las personas ha sufrido un increíble aumento, lo que ha provocado una gran demanda diaria de los medios de transporte público, y como consecuencia, del transporte ferroviario. Además, tal demanda sigue aumentando día a día en número y en exigencia, por lo que resulta fundamental lograr una planificación adecuada del servicio. El presente proyecto, se centra en una de las etapas necesarias para su consecución, concretamente, tiene el propósito de desarrollar e implementar un modelo de programación lineal mixta-entera que permita determinar las frecuencias óptimas junto con el resto de variables necesarias para programar cada una de las líneas que componen una red de cercanías, atendiendo a las características y exigencias que supone un sistema de transporte como el que se presenta. Adicionalmente, debido a que el modelo se propone para una red compuesta por diferentes líneas que cuentan con varias estaciones en común, los pasajeros tendrán en la mayoría de las ocasiones diferentes opciones o rutas para llegar a un mismo destino, y por ello, surge la necesidad de realizar una asignación de tránsito que nos permita ver cómo se comportan los usuarios dada la red de transporte desarrollada, es decir, que estrategias de viaje seleccionan para llegar al destino deseado. Los dos problemas presentados guardan una estrecha relación, ya que, en base a las frecuencias establecidas, los pasajeros decidirán escoger una ruta u otra, pero al mismo tiempo, en función del número de viajeros que utilicen una línea, se establecerá una determinada frecuencia de paso. Por lo tanto, se busca alcanzar un equilibrio entre ambos problemas. Así mismo, con el objetivo de satisfacer al mismo tiempo tanto a operadores como a pasajeros, se persigue en todo momento la minimización de los gastos de tripulación y de los costes de operación de los diferentes modelos de tren puestos en marcha, y, junto con ello, minimizar también el valor monet, Over the last decades, the people´s need for mobility has suffered an incredible increase, which has caused a great daily demand for public transport, and consequently, for rail transport. In addition, such demand continues to increase day by day in number and in request, so it is essential to achieve an adequate planning of the service. The present project focuses on one of the necessary stages for its achievement, specifically, it has the purpose of developing a linear programming model that allows determining the optimal frequencies along with the rest of necessary variables to schedule each of the lines that make up a commuter rail network, considering the characteristics and requests of a transport system such as the one presented. Additionally, due to the model is proposed for a network composed of different lines that have in common several stations, in most cases, passengers will have different options or routes to reach the same destination and, because of that, the need arises to make a transit assignment that allows us to see how the users behave given the transport network developed, that is, which travel strategies they select to reach the desired destination. The two problems presented are closely related, since, based on the established frequencies, passengers will choose one route or another, but at the same time, depending on the number of passengers using a line, a certain frequency will be established. Therefore, it is sought to find a balance between both problems. Likewise, with the objective of satisfying both operators and passengers at the same time, it is pursued the minimization of crew costs and operation costs of the different train models put in place, and, along with this, also minimize the monetary value of the total trip time used by passengers to reach their corresponding destinations, including not only on-board travel times, but also transfer times and waiting times. However, since both objectives are opposed, before choosing a fin

The collapse of infrastructures is very often a complicating factor for the early emergency actuations after a disaster. A proper plan to better cover the needs of the affected people within the disaster area while maintaining life-saving relief operations is mandatory hence. In this paper, we use a drone for flying over a set of difficult-to-access locations for imaging issues to get information to build a risk assessment as the earliest stage of the emergency operations. While the drone provides the flexibility required to visit subsequently a sort of isolated locations, it needs a commando vehicle in ground for (i) monitoring the deployment of operations and (ii) being a recharging station where the drone gets fresh batteries. This work proposes a decision-making process to plan the mission, which is composed by the ground vehicle stopping points and the sequence of locations visited for each drone route. We propose a Genetic Algorithm (GA) which has proven to be helpful in finding good solutions in short computing times. We provide experimental analysis on the factors effecting the performance of the output solutions, around an illustrative test instance. Results show the applicability of these techniques for providing proper solutions to the studied problem.

The collapse of infrastructures is very often a complicating factor for the early emergency actuations after a disaster. A proper plan to better cover the needs of the affected people within the disaster area while maintaining life-saving relief operations is mandatory hence. In this paper, we use a drone for flying over a set of difficult-to-access locations for imaging issues to get information to build a risk assessment as the earliest stage of the emergency operations. While the drone provides the flexibility required to visit subsequently a sort of isolated locations, it needs a commando vehicle in ground for (i) monitoring the deployment of operations and (ii) being a recharging station where the drone gets fresh batteries. This work proposes a decision-making process to plan the mission, which is composed by the ground vehicle stopping points and the sequence of locations visited for each drone route. We propose a Genetic Algorithm (GA) which has proven to be helpful in finding good solutions in short computing times. We provide experimental analysis on the factors effecting the performance of the output solutions, around an illustrative test instance. Results show the applicability of these techniques for providing proper solutions to the studied problem.