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Final, Resolved, 2023-2024, 1r quadrimestre

Nowadays, businesses are faced with an overwhelming amount of data. Companies are using data-driven solutions to improve decision making and stay competitive, especially in the supply chain industry where data management and analysis can improve operations and reduce costs. Accenture is no exception. As the amount of data increases, some internal libraries have become the bottleneck for large size client projects. In this work, we aim to improve the performance of the Network library used for generating graphs from supply chain data. Research is conducted on two popular graph tools (NetworkX and Neo4j) to find a suitable alternative. Results show that NetworkX offers a good balance between execution time and memory usage while Neo4j has fast query performance but high memory usage.

This thesis explores the application of quantum computing techniques to solve Quadratic Unconstrained Binary Optimization problems, with a focus on the Unit Commitment problem. The thesis provides an introduction to quantum computing, including its mathematical foundation and the distinction between classical and quantum systems. It then discusses Variational Quantum Algorithms and explores various quantum computing platforms. Then a novel formulation of the Unit Commitment problem is presented, along with its implementation using the Qiskit library. The results obtained from the implementation are summarized, highlighting the process of using quantum computing for solving optimization problems.

Final amb solucions, Resolved, 2022-2023, 1r quadrimestre

El consumo de nuez está muy extendido a nivel mundial debido a sus propiedades organolépticas y a su composición rica en compuestos bioactivos potencialmente beneficiosos para la salud. En este estudio se han desarrollado métodos no destructivos para el control varietal y la detección de desperfectos de nueces con cáscara a partir de análisis de imagen digital. Se ha obtenido un porcentaje de clasificación correcta en validación externa del 77% para la identificación de la variedad de nuez y del 100% para la detección de nueces defectuosas. Queda de manifiesto, por tanto, el gran potencial que tiene esta técnica para, analizando la nuez completa con cáscara, evaluar la defectología en su interior.

El proyecto tiene como objetivo la optimización de una cadena de suministro con demanda independiente estocástica a partir de un algoritmo genético. La cadena de suministro, al ser un sistema complejo que no se puede modelar a partir de una fórmula analítica por lo que se simulará, siendo los outputs de esta simulación la alimentación del algoritmo genético. Dentro del genético se intentará conseguir una buena funcionalidad implementando diferentes configuraciones y modelos para la inicialización, selección, crossover y mutación.

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This master thesis explore different multi-stage stochastic programming models for electricity generation companies to find optimal bid functions in electric spot markets. The explored models not only capture the uncertainty of prices of different markets and financial products, but also couples together wind and thermal generation units, offering producers that combine both technologies a more suitable approach to find their best possible bidding strategy among the space of possible actions.

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The presence of renewables in energy systems optimization have generated a high level of uncertainty in the data, which has led to a need for applying stochastic optimization to modelling problems with this characteristic. The method followed in this thesis is multistage Stochastic Programming (MSP). Central to MSP is the idea of representing uncertainty (which, in this case, is modelled with a stochastic process) using scenario trees. In this thesis, we developed a methodology that starts with available historical data; generates a set of scenarios for each random variable of the MSP model; define individual scenarios that are used to build the initial stochastic process (as a fan or an initial scenario tree); and builds the final scenario trees that are the approximation of the stochastic process. The methodology proposes consists of two phases. In the first phase, we developed a procedure similar to Muñoz et al. (2013), with the difference being that the VAR models are used to predict the next day for each random parameter of the MSP models. In the second phase, we build scenario trees from the Forward Tree Construction Algorithm(FTCA), developed by Heitsch and Römisch (2009a); and an adapted version of DynamicTree Generation with a Flexible Bushiness Algorithm (DTGFBA), developed by Pflugand Pichler (2014, 2015). This methodology was used to generate scenario trees for two MSP models. A first model, Multistage Stochastic Wind Battery Virtual Power Plantmodel (MSWBVPP model) and to a second model, which is the Multistage StochasticOptimal Operation of Distribution Networks model (MSOODN model). We developed extensive computational experiments for the MSWBVPP model and generated scenario trees with real data, which were based on MIBEL prices and wind power generation of the real wind farm called Espina, located in Spain. For the MSOODN model, we obtained scenario trees by also using real data from the power load provided by FEEC-UNICAMP and photovoltaic generation, a presencia de energías renovables en la optimización de sistemas energéticos hagenerado un alto nivel de incertidumbre en los datos, lo que ha llevado a la necesidad de aplicar técnicas de optimización estocástica para modelar problemas con estas características. El método empleado en esta tesis es programación estocástica multietapa (MSP, por sus siglas en inglés). La idea central de MSP es representar la incertidumbre (que en este caso es modelada mediante un proceso estocástico), mediante un árbol de escenarios. En esta tesis, desarrollamos una metodología que parte de una data histórica, la cual está disponible; generamos un conjunto de escenarios por cada variable aleatoria del modelo MSP; definimos escenarios individuales, que luego serán usados para construir el proceso estocástico inicial (como un fan o un árbol de escenario inicial); y, por último, construimos el árbol de escenario final, el cual es la aproximación del proceso estocástico. La metodología propuesta consta de dos fases. En la primera fase, desarrollamos un procedimiento similar a Muñoz et al. (2013), con la diferencia de que para las predicciones del próximo día para cada variable aleatoria del modelo MSP usamos modelos VAR. En la segunda fase construimos árboles de escenarios mediante el "Forward Tree Construction Algorithm (FTCA)", desarrollado por Heitsch and Römisch (2009a); y una versión adaptada del "Dynamic Tree Generation with a Flexible Bushiness Algorithm (DTGFBA)", desarrolado por Pflug and Pichler (2014, 2015). Esta metodología fue usada para generar árboles de escenarios para dos modelos MSP. El primer modelo fue el "Multistage Stochastic Wind Battery Virtual Power Plant model (modelo MSWBVPP)", y el segundo modelo es el "Multistage Stochastic Optimal Operation of Distribution Networks model (MSOODN model)". Para el modelo MSWBVPP desarrollamos extensivos experimentos computacionales y generamos árboles de escenarios a partir de datos realesde precios MIBEL y generación eólica d, Postprint (published version)

© 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0, European Directives are incentivizing consumers to play an active role in the electricity system and to collaborate to maintain its stability, which has been historically provided by large generation power plants. However, it is not easy for the System Operator to handle the coexistence of consumers and generators in the same markets. Under these circumstances, a new actor allows small residential and commercial consumers to participate in flexibility markets: The Demand Aggregator. However, balancing markets opened to Demand Aggregators still present several barriers that do not allow their practical participation. This study analyzes barriers and enablers of four European electricity markets and proposes a new market framework that would enhance Demand Aggregators’ participation. To validate the proposed market and to understand the economic potentials of aggregated small tertiary buildings, a Demand Aggregator is simulated using real building’s consumption data. Results show that technical requirements to participate in balancing markets such as the minimum bid size, the symmetricity of the offer and the product resolution strongly affect incomes for Demand Aggregators. However, neither in the proposed market, the creation of a Demand Aggregator whose business model is focused on small tertiary buildings does not seem realistic due to low incomes in comparison to the fixed costs necessary to enable Demand Response, especially if only the air conditioning system is considered., Peer Reviewed, Postprint (author's final draft)

Accenture, This thesis details the design and implementation of a sensitivity analysis module that runs in unison with a multi-echelon inventory optimization module in order to direct insights about the varying of inventory model parameters. Then, an in-depth sensitivity analysis is performed using the module and its assortment of visual aids to investigate correlations, interaction effects, conditional variances, and other sensitivity metrics for two different multi-echelon inventory models.

This study presents a new two-stage stochastic programming decision model for assessing how to introduce some new manufacturing technology into any generic supply and distribution chain. It additionally determines the optimal degree of postponement, as represented by the so-called customer order decoupling point (CODP), while assuming uncertainty in demand for multiple products. To this end, we propose here the formulation of a generic supply chain through an oriented graph that represents all the deployable alternative technologies, which are defined through a set of operations that are characterized by lead times and cost parameters. Based on this graph, we develop a mixed integer two-stage stochastic program that finds the optimal manufacturing technology for meeting each market’s demand, each operation’s optimal production quantity, and each selected technology’s optimal CODP. We also present and analyse a case study for introducing additive manufacturing technologies., This work was developed under an Accenture Open Innovation University [grant number I-01326] and was also partially supported by grant RTI2018-097580-B-I00 of the Ministry of Economy and Competitiveness of Spain., Peer Reviewed, Postprint (published version)

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In this master s thesis, it is presented the problem of mitigating the damage caused in a landscape by a wildfire. Specifically, in the problem, wildfire managers aim to minimize the impact of a worst-case wildfire on the landscape, by implementing fuel treatment. A min-max interdiction model is formulated. Regarding the solving method, the original problem is reformulated decomposing it into two subproblems which are solved alternatively, one of them exactly and the other one with a heuristic procedure, in an iterative algorithm. The problem, the formulation, and the solving method are described, and the results for a real test case are analysed.

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A 120–140 GHz frequency-switchable, very compact low-noise amplifier (LNA) fabricated in a 0.13 µm SiGe:C BiCMOS technology is proposed. A single radio-frequency (RF) switch composed of three parallel hetero junction bipolar transistors (HBTs) in a common-collector configuration and a multimodal three-line microstrip structure in the input matching network are used to obtain a LNA chip of miniaturized size. A systematic design procedure is applied to obtain a perfectly balanced gain and noise figure in both frequency states (120 GHz and 140 GHz). The measured gain and noise figure are 14.2/14.2 dB and 8.2/8.2 dB at 120/140 GHz respectively, in very good agreement with circuit/electromagnetic co-simulations. The LNA chip and core areas are 0.197 mm2 and 0.091 mm2, respectively, which supposes an area reduction of 23.4% and 15.2% compared to other LNAs reported in this frequency band. The experimental results validate the design procedure and its analysis, Peer Reviewed, Postprint (published version)

El sistema de Mercats d’Electricitat (EM) es creà amb la finalitat de generar, transportar i distribuir energia elèctrica per satisfer la demanda a nivell nacional en cada moment. Aquest organisme inclou diferents mercats que interaccionen amb les plantes eòliques, les quals venen l’energia a aquests mercats. En aquets context, l’ús d’arbres d’escenaris ha de permetre a les plantes obtenir informació útil del comportament dels mercats per aconseguir el màxim de guanys en la venda d’energia. L’objectiu principal d’aquest projecte és l’obtenció d’arbres d’escenaris mitjançant algorismes de generació dinàmica per tal de satisfer la necessitat de resoldre, en un temps raonable, problemes complexos d’optimització d’oferta òptima de plantes eòliques en mercats elèctrics., The main purpose of this project is to obtain scenario trees using a dynamic generation algorithm in order to satisfy the need to solve, in a reasonable period of time, complex optimization problems of optimal supply of wind power plants in electricity markets. The scenarios trees that are going to be obtained include information regarding the production of wind power energy and the clearing prices of the auctions of the different studied electricity markets. In this study a scenario tree generation algorithm is presented, together with all the theoretical background needed to understand and assure a correct implementation of it, as well as the definition of the different agents that perform in the context of electricity markets. In addition to that, some data will be applied to this algorithm in order to obtain a representation of the scenario trees and to understand the interpretation of them.

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© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0, The recent cost reduction and technological advances in medium- to large-scale battery energy storage systems (BESS) makes these devices a true alternative for wind producers operating in electricity markets. Associating a wind power farm with a BESS (the so-called virtual power plant (VPP)) provides utilities with a tool that converts uncertain wind power production into a dispatchable technology that can operate not only in spot and adjustment markets (day-ahead and intraday markets) but also in ancillary services markets that, up to now, were forbidden to non-dispatchable technologies. What is more, recent studies have shown capital cost investment in BESS can be recovered only by means of such a VPP participating in the ancillary services markets. We present in this study a multi-stage stochastic programming model to find the optimal operation of a VPP in the day-ahead, intraday and secondary reserve markets while taking into account uncertainty in wind power generation and clearing prices (day-ahead, secondary reserve, intraday markets and system imbalances). A case study with real data from the Iberian electricity market is presented., Peer Reviewed, Postprint (author's final draft)

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El projecte contindrà 3 grans blocs diferenciats: 1. Un primer bloc d'introducció al funcionament del mercat elèctric i l'explicació del sistema de la casació als mercats diari i intradiari (on s'explicarà el nou sistema del mercat intradiari que entrarà en vigor el juny de 2018). 2. El bloc principal del projecte, que és l`estudi i la definició en termes d'optimització i programació matemàtica del sistema EUPHEMIA com a algoritme que governa la optimització de la casació als mercats elèctrics europeus i, concretament, el mercat estatal. En aquest bloc, l'alumne haurà de cursar o estudiar el material per assolir els coneixements necessaris, l'assignatura Energy Markets - Optimization of Electricity Markets Operations del `Master in Energy Engineering de la UPC, amb un enfoc d'optimització i programació matemàtica en el modelat d'una eina amb una funció aproximada a la del sistema EUPHEMIA. 3. Estudi de l'impacte de diversos escenaris en la casació utilitzant l'eina prèviament definida. 1) Anàlisi de l'impacte dels diferents escenaris de transició energètica en el preu de casació. Per exemple amb o sense nuclear, cas catalunya/estat, amb diferents nivells d'interconnexió internacional, escenaris d'hidraulicitat severa... 2) Anàlisi de la viabilitat d'agregació de recursos d'energia distribuits al nou mercat intradiari. Per exemple, potencial econòmic de la gestió de la demanda i/o bateries d'acord amb els preus del mercat intradiari.

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0, There are many factors that influence the day-ahead market bidding strategies of a generation company (GenCo) within the framework of the current energy market. Environmental policy issues are giving rise to emission limitation that are becoming more and more important for fossil-fueled power plants, and these must be considered in their management. This work investigates the influence of the emissions reduction plan and the incorporation of the medium-term derivative commitments in the optimal generation bidding strategy for the day-ahead electricity market. Two different technologies have been considered: the high-emission technology of thermal coal units and the low-emission technology of combined cycle gas turbine units. The Iberian Electricity Market (MIBEL) and the Spanish National Emissions Reduction Plan (NERP) defines the environmental framework for dealing with the day-ahead market bidding strategies. To address emission limitations, we have extended some of the standard risk management methodologies developed for financial markets, such as Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR), thus leading to the new concept of Conditional Emission at Risk (CEaR). This study offers electricity generation utilities a mathematical model for determining the unit's optimal generation bid to the wholesale electricity market such that it maximizes the long-term profits of the utility while allowing it to abide by the Iberian Electricity Market rules as well as the environmental restrictions set by the Spanish National Emissions Reduction Plan. We analyze the economic implications for a GenCo that includes the environmental restrictions of this National Plan as well as the NERP's effects on the expected profits and the optimal generation bid., Peer Reviewed, Postprint (author's final draft)

The aim of this Master's Degree Thesis is to propose a solution for rebalancing stocks among retail points of sale of one of the UK's leading retailers. Each day, a fleet of vehicles serves a set of locations with a variety of products. The fact that the vehicles follow pre-established routes leads to frequent lost sales among those points of sale that are later served. In this Thesis, an end-to-end solution is proposed to solve a variant of the VRP that takes into account both transportation and lost sales costs: - The dynamic and deterministic version of the problem is solved using exact formulations and metaheuristics (Genetic Algorithm and Greedy Randomized Adaptative Search Procedure). - A visualization tool is implemented combining R Shiny, AMPL and MySQL to show the behavior of the solution.

IREC

El present document pretén ser una base acadèmica i empresarial per a la comprensió de les diferents estratègies de compra d’electricitat disponibles a l’estat espanyol, a les quals poden acudir diferents agents de mercat, entre ells les comercialitzadores. La motivació sorgida per analitzar els mercats elèctrics s’origina després de l’experiència en el món laboral, ja que la formació dels preus d’electricitat al mercat majorista és encara un tema tabú per molts enginyers i gestors energètics de diferents empreses. Moltes d’elles desconeixen quines són les estratègies de compra d’electricitat adequades en cada moment. La introducció situarà el marc teòric i les diferents estratègies de transaccions per obtenir energia elèctrica: caldrà tenir un marc històric per entendre la situació actual, un marc legal per conèixer el procediment operatiu de cada estratègia i un marc estratègic amb la finalitat de considerar les variables que influencien en el preu de cada estratègia.. El dos següents apartats contindran la mateixa estructura analítica, basats en les dos estratègies de compra d’electricitat més conegudes: el Mercat Diari (MD) i el Mercat a Termini (MT). En cada cas es detallarà les evolucions dels preus d’electricitat i les variables que afecten el seu comportament. Com a complement al MD s’afegirà un capítol analitzant les característiques més importants dels Contractes Bilaterals, una estratègia de compra d’electricitat que està guanyant pes progressivament. El cinquè capítol anirà enfocat a examinar l’evolució dels preus de les dues estratègies de compra més utilitzades a l’estat durant el 2016. S’observaran diferents períodes de decisió per determinar les diferències econòmiques que podria haver tingut una comercialitzadora que ha d’abastir-se d’energia elèctrica. En aquest apartat es pretén mostrar com, segons el moment de compra, és preferible optar per una estratègia o altre, contrastant amb les variables exposades als capítols anteriors. Els límits tempora

El problema del unit commitment (UC) és un dels problemes combinatoris de major rellevància en l'aplicació de l'optimització matemàtica als sistemes d'energia i mercats elèctrics, essent fonamental per a la resolució eficient dels problemes d'optimització combinatòria no lineal on apareixen. El UC a curt termini és una de les tasques crítiques actuals als que s enfronten diàriament diferents tipus de tècnics del sector elèctric. En general, el principal objectiu és casar la demanda amb cost mínim (o, altrament maximitzar els beneficis que són funcions objectius complementàries) mentre el sistema està en funcionament, conjuntament amb el compliment de les característiques tècniques de cada unitat.

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The best-selling products manufactured nowadays are made in long series along rigid product value chains. Product repetition and continuous/stable manufacturing is seen as a chance for achieving economies of scale. Nevertheless, these speculative strategies fail to meet special customer demands, thus reducing the effective market share of a product in a range. Additive Manufacturing technologies open promising product customization opportunities; however, to achieve it, it is necessary to delay the production operations in order to incorporate the customer’s inputs in the product materialization. The study offered in the present paper compares different possible production strategies for a product (via conventional technologies and Additive Manufacturing) and assesses the degree of postponement that it would be recommended in order to meet a certain demand distribution. The problem solving is calculated by a program containing a stochastic mathematical model which incorporates extensive information on costs and lead times for the required manufacturing operations., Postprint (published version)

Communities are a fundamental structure in social organizations. It is not surprising, then, that there have been multiple attempts at modeling them in order to better understand the architecture of our civilization. Usually, these models are based on online Social Networks, which provide incredibly valuable data about communities that are believed to be significantly similar to real-life communities. Large scale models have provided insight in several macroscopic properties of these networks. There is, however, very little work on the microscopic properties of communities and their role in Social Networks, leaving an important research gap that is being fulfilled during recent years. The objective of this thesis is come up with this kind of hypotheses and analyse their role in a mathematical model., Outgoing

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Hoy en día los costos logísticos representan una gran oportunidad de mejora para las empresas siendo los costos de transporte y los costos de inventario los más representativos. En este trabajo se presenta un estudio de una empresa ubicada en la región, la cual incurre actualmente en altos costos logísticos en su proceso de importación de materia prima desde Asia hasta su filial en Monterrey, N.L. Por medio de un modelo matemático entero mixto se consigue minimizar los costos antes mencionados. El modelo tiene las siguientes características: es de ubicación de facilidades en cuatro etapas, multiproducto, multiperiodo y multitransporte., Peer Reviewed, Postprint (published version)

Discrete Optimization Group (EPFL)