High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

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 new model for developing capacity expansion strategies

Nove širokopojasne tehnologije i stalno rastući obim saobraćaja imaju veliki uticaj na upravljanje infrastrukturnim kapacitetima telekomunikacione mreže. Efikasno planiranje mrežnih resursa nije moguće realizovati bez primene pouzdanih modela za prognoziranje tražnje, kao i precizno odabranih veličina za karakterizaciju saobraćaja. U tom smislu, osnovni predmet istraživanja ove doktorske disertacije su strategije proširenja mrežnih resursa, koje se oslanjaju na primenu različitih metoda planiranja telekomunikacionih mreža, sa ciljem određivanja optimalnog vremena za realizaciju proširenja. Predloženi modeli proširenja resursa mreže baziraju se na optimizaciji perioda proširenja resursa mreže uz minimizaciju troškova zagušenja i troškova neophodne opreme, pri čemu se ulazni podaci oslanjaju na prognozirane vrednosti tražnje na telekomunikacionom tržištu. Mogućnosti primene različitih metoda planiranja, u kontekstu strategije proširenja mrežnih resursa, razmatrane su na hipotetičkoj WDM mreži, sa koegzistencijom fiksnih i fleksibilnih grid tehnologija. U disertaciji su razmatrani suprotstavljeni zahtevi investicionih ulaganja u cilju brzog povraćaja uloženih sredstava i kvaliteta opsluživanja korisnika. Kompromisno rešenje predstavlja i novi pristup, predložen u disertaciji, koji se odnosi na izbor parametara za karakterizaciju mrežnog saobraćaja, sa ciljem smanjenja uticaja neizvesnosti prognoziranih podataka na odluku o proširenju kapaciteta. U tom smislu, izvršena je analiza, na osnovu koje je utvrđeno da se, poređenjem vrednosti nivoa penala funkcije troškova zagušenja, sa jedne strane i vrednosti parametra Verovatnoće blokiranja kapaciteta, sa druge, može odrediti optimalni vremenski period za sprovođenje odluke o migraciji tehnologija, u posmatranom slučaju. Među najvažnijim naučnim doprinosima disertacije, mogu se izdvojiti implementacija prognoziranih vrednosti saobraćajnih zahteva, zasnovana na primeni teorije difuzionih modela, novi optimizacioni model, kao modifikacija modela strategije serijskog proširenja kapaciteta mrežnog linka, u kojem se uvodi mogućnost prelaska na novu tehnologiju, kao i novi pristup za smanjenje uticaja neizvesnosti prognoziranih podataka na određenu odluku o proširenju kapaciteta, zasnovan na izboru parametara za karakterizaciju mrežnog saobraćaja.New broadband services and ever-increasing Internet traffic volumes have a major influence on the backbone infrastructure capacity management, raising the importance of efficient planning of network resources, based on demand forecast and the appropriate selection of traffic characterization parameters, as well. Therefore, the main subject of the research, presented in the doctoral thesis are the network resources expansion strategies, with respect of different telecommunication network planning methods. Relaying on the forecasted demand in the telecommunications market, the proposed models are used in order to determine the optimal times for network capacity expansion and the technology migration, by minimizing the equipment and congestion costs. Application of different planning methods, in the context of the network resource expansion strategies, has been considered within the hypothetical WDM network, with the coexistence of fixed and flexible grid technologies. Having in mind the contrast between network performance improvement and the investors’ interest for the longer operation of already built-in technologies, a novel approach has been proposed in order to determine the appropriate time for making the technology migration. In this sense, the traffic characterization parameters (the Penalty function and the Bandwidth Blocking Ratio) were used. The main innovative aspect of the approach proposed in the thesis considers the combination of these two metrics in order to decrease uncertainty of the forecasted demands. The other important scientific contributions of the dissertation are the traffic demand forecasting approach based on the diffusion model theory, as well as the novel multiperiod network capacity expansion model, which introduces the option of the migration to the new technology

Optimization Methods Applied to Power Systems Ⅱ

Electrical power systems are complex networks that include a set of electrical components that allow distributing the electricity generated in the conventional and renewable power plants to distribution systems so it can be received by final consumers (businesses and homes). In practice, power system management requires solving different design, operation, and control problems. Bearing in mind that computers are used to solve these complex optimization problems, this book includes some recent contributions to this field that cover a large variety of problems. More specifically, the book includes contributions about topics such as controllers for the frequency response of microgrids, post-contingency overflow analysis, line overloads after line and generation contingences, power quality disturbances, earthing system touch voltages, security-constrained optimal power flow, voltage regulation planning, intermittent generation in power systems, location of partial discharge source in gas-insulated switchgear, electric vehicle charging stations, optimal power flow with photovoltaic generation, hydroelectric plant location selection, cold-thermal-electric integrated energy systems, high-efficiency resonant devices for microwave power generation, security-constrained unit commitment, and economic dispatch problems

Efficient Algorithms for Infrastructure Networks: Planning Issues and Economic Impact

Mei, R.D. van der [Promotor]Bhulai, S. [Copromotor