Optimizing energy consumption in smart cities’ mobility: electric vehicles, algorithms, and collaborative economy

Mobility and transportation activities in smart cities require an increasing amount of energy. With the frequent energy crises arising worldwide and the need for a more sustainable and environmental friendly economy, optimizing energy consumption in these growing activities becomes a must. This work reviews the latest works in this matter and discusses several challenges that emerge from the aforementioned social and industrial demands. The paper analyzes how collaborative concepts and the increasing use of electric vehicles can contribute to reduce energy consumption practices, as well as intelligent x-heuristic algorithms that can be employed to achieve this fundamental goal. In addition, the paper analyzes computational results from previous works on mobility and transportation in smart cities applying x-heuristics algorithms. Finally, a novel computational experiment, involving a ridesharing example, is carried out to illustrate the benefits that can be obtained by employing these algorithms.Peer ReviewedPostprint (published version

A simheuristic for routing electric vehicles with limited driving ranges and stochastic travel times

Green transportation is becoming relevant in the context of smart cities, where the use of electric vehicles represents a promising strategy to support sustainability policies. However the use of electric vehicles shows some drawbacks as well, such as their limited driving-range capacity. This paper analyses a realistic vehicle routing problem in which both driving-range constraints and stochastic travel times are considered. Thus, the main goal is to minimize the expected time-based cost required to complete the freight distribution plan. In order to design reliable Routing plans, a simheuristic algorithm is proposed. It combines Monte Carlo simulation with a multi-start metaheuristic, which also employs biased-randomization techniques. By including simulation, simheuristics extend the capabilities of metaheuristics to deal with stochastic problems. A series of computational experiments are performed to test our solving approach as well as to analyse the effect of uncertainty on the routing plans.Peer Reviewe

GENETIC ALGORITHM WITH GREEDY CROSSOVER AND ELITISM FOR CAPACITY PLANNING

We propose a modification to the genetic algorithm with greedy agglomerative crossover operator for the problem of scheduling product types at the facilities of the metal or plastic production factory where the goal is to minimize the number of switchings of the product type of the production lines. Similar algorithms with greedy agglomerative crossover for location problems do not use any elitism in the population. For the considered problem which may also be classified as a location problem, elitism in the population implemened in the form of tournament selection plays a positive role.  The article also discusses the dependence of the efficiency of the evolutionary algorithm on the size of the population.   As our experiments show, the introduction of elitism into such an algorithm enables us to increase both the rate of convergence of the algorithm and the accuracy of the solution. A special aspect chooses an individual with the best value of the objective function

Memòria 2014 Biblioteca EPSEB

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Applications of biased-randomized algorithms and simheuristics in integrated logistics

Transportation and logistics (T&L) activities play a vital role in the development of many businesses from different industries. With the increasing number of people living in urban areas, the expansion of on-demand economy and e-commerce activities, the number of services from transportation and delivery has considerably increased. Consequently, several urban problems have been potentialized, such as traffic congestion and pollution. Several related problems can be formulated as a combinatorial optimization problem (COP). Since most of them are NP-Hard, the finding of optimal solutions through exact solution methods is often impractical in a reasonable amount of time. In realistic settings, the increasing need for 'instant' decision-making further refutes their use in real life. Under these circumstances, this thesis aims at: (i) identifying realistic COPs from different industries; (ii) developing different classes of approximate solution approaches to solve the identified T&L problems; (iii) conducting a series of computational experiments to validate and measure the performance of the developed approaches. The novel concept of 'agile optimization' is introduced, which refers to the combination of biased-randomized heuristics with parallel computing to deal with real-time decision-making.Las actividades de transporte y logística (T&L) juegan un papel vital en el desarrollo de muchas empresas de diferentes industrias. Con el creciente número de personas que viven en áreas urbanas, la expansión de la economía a lacarta y las actividades de comercio electrónico, el número de servicios de transporte y entrega ha aumentado considerablemente. En consecuencia, se han potencializado varios problemas urbanos, como la congestión del tráfico y la contaminación. Varios problemas relacionados pueden formularse como un problema de optimización combinatoria (COP). Dado que la mayoría de ellos son NP-Hard, la búsqueda de soluciones óptimas a través de métodos de solución exactos a menudo no es práctico en un período de tiempo razonable. En entornos realistas, la creciente necesidad de una toma de decisiones "instantánea" refuta aún más su uso en la vida real. En estas circunstancias, esta tesis tiene como objetivo: (i) identificar COP realistas de diferentes industrias; (ii) desarrollar diferentes clases de enfoques de solución aproximada para resolver los problemas de T&L identificados; (iii) realizar una serie de experimentos computacionales para validar y medir el desempeño de los enfoques desarrollados. Se introduce el nuevo concepto de optimización ágil, que se refiere a la combinación de heurísticas aleatorias sesgadas con computación paralela para hacer frente a la toma de decisiones en tiempo real.Les activitats de transport i logística (T&L) tenen un paper vital en el desenvolupament de moltes empreses de diferents indústries. Amb l'augment del nombre de persones que viuen a les zones urbanes, l'expansió de l'economia a la carta i les activitats de comerç electrònic, el nombre de serveis del transport i el lliurament ha augmentat considerablement. En conseqüència, s'han potencialitzat diversos problemes urbans, com ara la congestió del trànsit i la contaminació. Es poden formular diversos problemes relacionats com a problema d'optimització combinatòria (COP). Com que la majoria són NP-Hard, la recerca de solucions òptimes mitjançant mètodes de solució exactes sovint no és pràctica en un temps raonable. En entorns realistes, la creixent necessitat de prendre decisions "instantànies" refuta encara més el seu ús a la vida real. En aquestes circumstàncies, aquesta tesi té com a objectiu: (i) identificar COP realistes de diferents indústries; (ii) desenvolupar diferents classes d'aproximacions aproximades a la solució per resoldre els problemes identificats de T&L; (iii) la realització d'una sèrie d'experiments computacionals per validar i mesurar el rendiment dels enfocaments desenvolupats. S'introdueix el nou concepte d'optimització àgil, que fa referència a la combinació d'heurístiques esbiaixades i aleatòries amb informàtica paral·lela per fer front a la presa de decisions en temps real.Tecnologies de la informació i de xarxe

A Real-time Energy-Saving Mechanism in Internet of Vehicles Systems

Emerging technologies, such as self-driving cars and 5G communications, are raising new mobility and transportation possibilities in smart and sustainable cities, bringing to a new echo-system often referred to as Internet of Vehicles (IoV). In order to efficiently operate, an IoV system should take into account more stringent requirements with respect to traditional IoT systems, e.g., ultra-broadband connections, high-speed mobility, high-energy efficiency and requires efficient real-time algorithms. This paper proposes an energy and communication driven model for IoV scenarios, where roadside units (RSUs) need to be frequently assigned and re-assigned to the operating vehicles. The problem has been formulated as an Uncapacitated Facility Location Problem (UFLP) for jointly solving the RSU-to-vehicle allocation problem while managing the RSUs switch-on and -off processes. Differently from traditional UFLP approaches, based on static solutions, we propose here a fast-heuristic approach, based on a dynamic multi-period time scale mapping: the proposed algorithm is able to efficiently manage in real-time the RSUs, selecting at each period those to be activated and those to be switched off. The resulting methodology is tested against a set of benchmark instances, which allows us to illustrate its potential. Results, in terms of overall cost –mapping both energy consumption and transmission delays–, number of active RSUs, and convergence speed, are compared with static approaches, showing the effectiveness of the proposed dynamic solution. It is noticeable a gain of up to 11% in terms of overall cost with respect to the static approaches, with a moderate additional delay for finding the solution, around 0.8 s, while the overall number of RSUs to be switched on is sensibly reduced up to a fraction of 15% of the overall number of deployed RSUs, in the most convenient scenario

A Real-Time Energy-Saving Mechanism in Internet of Vehicles Systems

[EN] Emerging technologies, such as self-driving cars and 5G communications, are raising new mobility and transportation possibilities in smart and sustainable cities, bringing to a new echo-system often referred to as Internet of Vehicles (IoV). In order to efficiently operate, an IoV system should take into account more stringent requirements with respect to traditional IoT systems, e.g., ultra-broadband connections, high-speed mobility, high-energy efficiency and requires efficient real-time algorithms. This paper proposes an energy and communication driven model for IoV scenarios, where roadside units (RSUs) need to be frequently assigned and re-assigned to the operating vehicles. The problem has been formulated as an Uncapacitated Facility Location Problem (UFLP) for jointly solving the RSU-to-vehicle allocation problem while managing the RSUs switch-on and -off processes. Differently from traditional UFLP approaches, based on static solutions, we propose here a fast-heuristic approach, based on a dynamic multi-period time scale mapping: the proposed algorithm is able to efficiently manage in real-time the RSUs, selecting at each period those to be activated and those to be switched off. The resulting methodology is tested against a set of benchmark instances, which allows us to illustrate its potential. Results, in terms of overall cost-mapping both energy consumption and transmission delays-, number of active RSUs, and convergence speed, are compared with static approaches, showing the effectiveness of the proposed dynamic solution. It is noticeable a gain of up to 11% in terms of overall cost with respect to the static approaches, with a moderate additional delay for finding the solution, around 0.8 s, while the overall number of RSUs to be switched on is sensibly reduced up to a fraction of 15% of the overall number of deployed RSUs, in the most convenient scenario.The work of Luca Cesarano and Andrea Croce has been done during an abroad study period at Universitat Oberta de Catalunya, Spain, supported by Erasmus+ Study Programme of the European Union.Cesarano, L.; Croce, A.; Martins, LDC.; Tarchi, D.; Juan-Pérez, ÁA. (2021). A Real-Time Energy-Saving Mechanism in Internet of Vehicles Systems. IEEE Access. 9:157842-157858. https://doi.org/10.1109/ACCESS.2021.3130125157842157858

Internet of Vehicles and Real-Time Optimization Algorithms: Concepts for Vehicle Networking in Smart Cities

Achieving sustainable freight transport and citizens’ mobility operations in modern cities are becoming critical issues for many governments. By analyzing big data streams generated through IoT devices, city planners now have the possibility to optimize traffic and mobility patterns. IoT combined with innovative transport concepts as well as emerging mobility modes (e.g., ridesharing and carsharing) constitute a new paradigm in sustainable and optimized traffic operations in smart cities. Still, these are highly dynamic scenarios, which are also subject to a high uncertainty degree. Hence, factors such as real-time optimization and re-optimization of routes, stochastic travel times, and evolving customers’ requirements and traffic status also have to be considered. This paper discusses the main challenges associated with Internet of Vehicles (IoV) and vehicle networking scenarios, identifies the underlying optimization problems that need to be solved in real time, and proposes an approach to combine the use of IoV with parallelization approaches. To this aim, agile optimization and distributed machine learning are envisaged as the best candidate algorithms to develop efficient transport and mobility systems

Optimizing transportation systems and logistics network configurations : From biased-randomized algorithms to fuzzy simheuristics

242 páginasTransportation and logistics (T&L) are currently highly relevant functions in any competitive industry. Locating facilities or distributing goods to hundreds or thousands of customers are activities with a high degree of complexity, regardless of whether facilities and customers are placed all over the globe or in the same city. A countless number of alternative strategic, tactical, and operational decisions can be made in T&L systems; hence, reaching an optimal solution –e.g., a solution with the minimum cost or the maximum profit– is a really difficult challenge, even by the most powerful existing computers. Approximate methods, such as heuristics, metaheuristics, and simheuristics, are then proposed to solve T&L problems. They do not guarantee optimal results, but they yield good solutions in short computational times. These characteristics become even more important when considering uncertainty conditions, since they increase T&L problems’ complexity. Modeling uncertainty implies to introduce complex mathematical formulas and procedures, however, the model realism increases and, therefore, also its reliability to represent real world situations. Stochastic approaches, which require the use of probability distributions, are one of the most employed approaches to model uncertain parameters. Alternatively, if the real world does not provide enough information to reliably estimate a probability distribution, then fuzzy logic approaches become an alternative to model uncertainty. Hence, the main objective of this thesis is to design hybrid algorithms that combine fuzzy and stochastic simulation with approximate and exact methods to solve T&L problems considering operational, tactical, and strategic decision levels. This thesis is organized following a layered structure, in which each introduced layer enriches the previous one.El transporte y la logística (T&L) son actualmente funciones de gran relevancia en cual quier industria competitiva. La localización de instalaciones o la distribución de mercancías a cientos o miles de clientes son actividades con un alto grado de complejidad, indepen dientemente de si las instalaciones y los clientes se encuentran en todo el mundo o en la misma ciudad. En los sistemas de T&L se pueden tomar un sinnúmero de decisiones al ternativas estratégicas, tácticas y operativas; por lo tanto, llegar a una solución óptima –por ejemplo, una solución con el mínimo costo o la máxima utilidad– es un desafío realmente di fícil, incluso para las computadoras más potentes que existen hoy en día. Así pues, métodos aproximados, tales como heurísticas, metaheurísticas y simheurísticas, son propuestos para resolver problemas de T&L. Estos métodos no garantizan resultados óptimos, pero ofrecen buenas soluciones en tiempos computacionales cortos. Estas características se vuelven aún más importantes cuando se consideran condiciones de incertidumbre, ya que estas aumen tan la complejidad de los problemas de T&L. Modelar la incertidumbre implica introducir fórmulas y procedimientos matemáticos complejos, sin embargo, el realismo del modelo aumenta y, por lo tanto, también su confiabilidad para representar situaciones del mundo real. Los enfoques estocásticos, que requieren el uso de distribuciones de probabilidad, son uno de los enfoques más empleados para modelar parámetros inciertos. Alternativamente, si el mundo real no proporciona suficiente información para estimar de manera confiable una distribución de probabilidad, los enfoques que hacen uso de lógica difusa se convier ten en una alternativa para modelar la incertidumbre. Así pues, el objetivo principal de esta tesis es diseñar algoritmos híbridos que combinen simulación difusa y estocástica con métodos aproximados y exactos para resolver problemas de T&L considerando niveles de decisión operativos, tácticos y estratégicos. Esta tesis se organiza siguiendo una estructura por capas, en la que cada capa introducida enriquece a la anterior. Por lo tanto, en primer lugar se exponen heurísticas y metaheurísticas sesgadas-aleatorizadas para resolver proble mas de T&L que solo incluyen parámetros determinísticos. Posteriormente, la simulación Monte Carlo se agrega a estos enfoques para modelar parámetros estocásticos. Por último, se emplean simheurísticas difusas para abordar simultáneamente la incertidumbre difusa y estocástica. Una serie de experimentos numéricos es diseñada para probar los algoritmos propuestos, utilizando instancias de referencia, instancias nuevas e instancias del mundo real. Los resultados obtenidos demuestran la eficiencia de los algoritmos diseñados, tanto en costo como en tiempo, así como su confiabilidad para resolver problemas realistas que incluyen incertidumbre y múltiples restricciones y condiciones que enriquecen todos los problemas abordados.Doctorado en Logística y Gestión de Cadenas de SuministrosDoctor en Logística y Gestión de Cadenas de Suministro