Multiobjective Genetic Algorithm applied to dengue control

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Reconfiguração de sistemas de distribuição de energia elétrica utilizando uma metodologia multipartida

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Um Modelo de otimização inteira mista na programação de produção de mangueiras hidráulicas

This work deals with a problem of mixed integer optimization model applied to production planning of a real world factory that aims for hydraulic hose production. To optimize production planning, a mathematic model of MILP Mixed Integer Linear Programming, so that, along with the Analytic Hierarchy process method, would be possible to create a hierarchical structure of the most import criteria for production planning, thus finding through a solving software the optimum hose attribution to its respective machine. The hybrid modeling of Analytic Hierarchy Process along with Linear Programming is the focus of this work. The results show that using this method we could unite factory reality and quantitative analysis and had success on improving performance of production planning efficiency regarding product delivery and optimization of the production flow

Um Modelo de otimização inteira mista na programação de produção de mangueiras hidráulicas

This work deals with a problem of mixed integer optimization model applied to production planning of a real world factory that aims for hydraulic hose production. To optimize production planning, a mathematic model of MILP Mixed Integer Linear Programming, so that, along with the Analytic Hierarchy process method, would be possible to create a hierarchical structure of the most import criteria for production planning, thus finding through a solving software the optimum hose attribution to its respective machine. The hybrid modeling of Analytic Hierarchy Process along with Linear Programming is the focus of this work. The results show that using this method we could unite factory reality and quantitative analysis and had success on improving performance of production planning efficiency regarding product delivery and optimization of the production flow

La qualità erogata da un'azienda di trasporto collettivo in ambito urbano: calcolo del livello di servizio delle principali linee di ATC (BO)

Oggetto di questa tesi è lo studio della qualità del servizio di trasporto erogato che condiziona la qualità percepita dall’utente, poiché spesso proprio a causa di un errato processo di pianificazione e gestione della rete, molte aziende non sono in grado di consolidare un alto livello di efficienza che permetta loro di attrarre e servire la crescente domanda. Per questo motivo, si è deciso di indagare sugli aspetti che determinano la qualità erogata e sui fattori che la influenzano, anche attraverso la definizione di alcuni indicatori rappresentativi del servizio erogato. L’area di studio considerata è stata quella urbana di Bologna, e sono state prese in esame due linee di ATC, la 19 e la 27, caratterizzate entrambe da una domanda di trasporto molto elevata. L’interesse è ricaduto in modo particolare sugli aspetti legati alla regolarità del servizio, ovvero al rispetto della cadenza programmata delle corse e alla puntualità, ossia il rispetto dell’orario programmato delle stesse. Proprio da questi due aspetti, infatti, dipende in larga misura la percezione della qualità che gli utenti hanno del servizio di trasporto collettivo. Lo studio è stato condotto sulla base di dati raccolti attraverso due campagne di rilevamento, una effettuata nel mese di maggio dell’anno 2008 e l’altra nel mese di settembre dello stesso anno. La scelta del periodo, della zona e delle modalità di rilevamento è strettamente connessa all’obiettivo prefissato. Il servizio è influenzato dalle caratteristiche del sistema di trasporto: sia da quelle legate alla domanda che da quelle legate all’offerta. Nel caso della domanda di trasporto si considera l’influenza sul servizio del numero di passeggeri saliti e del tempo di sosta alle fermate. Nel caso dell’offerta di trasporto si osservano soprattutto gli aspetti legati alla rete di trasporto su cui si muovono gli autobus, analizzando quindi i tempi di movimento e le velocità dei mezzi, per vedere come le caratteristiche dell’infrastruttura possano condizionare il servizio. A tale proposito è opportuno dire che, mentre i dati della prima analisi ci sono utili per lo studio dell’influenza del tempo di sosta sull’intertempo, nella seconda analisi si vuole cercare di effettuare ulteriori osservazioni sull’influenza del tempo di movimento sulla cadenza, prendendo in esame altri elementi, come ad esempio tratti di linea differenti rispetto al caso precedente. Un’attenzione particolare, inoltre, verrà riservata alla verifica del rispetto della cadenza, dalla quale scaturisce la definizione del livello di servizio per ciò che riguarda la regolarità. Per quest’ultima verrà, inoltre, determinato anche il LOS relativo alla puntualità. Collegato al problema del rispetto della cadenza è il fenomeno dell’accodamento: questo si verifica quando i mezzi di una stessa linea arrivano contemporaneamente ad una fermata uno dietro l’altro. L’accodamento ha, infatti, origine dal mancato rispetto della cadenza programmata tra i mezzi ed è un’evidente manifestazione del mal funzionamento di un servizio di trasporto. Verrà infine condotta un’analisi dei fattori che possono influenzare le prestazioni del servizio di trasporto pubblico, così da collocare i dati ottenuti dalle operazioni di rilevamento in un quadro più preciso, capace di sottolineare alcuni elementi di criticità e possibili rapporti di causalità.

Tecniche per il controllo dinamico del consumo di potenza per piattaforme system-on-chip

Providing support for multimedia applications on low-power mobile devices remains a significant research challenge. This is primarily due to two reasons: • Portable mobile devices have modest sizes and weights, and therefore inadequate resources, low CPU processing power, reduced display capabilities, limited memory and battery lifetimes as compared to desktop and laptop systems. • On the other hand, multimedia applications tend to have distinctive QoS and processing requirementswhichmake themextremely resource-demanding. This innate conflict introduces key research challenges in the design of multimedia applications and device-level power optimization. Energy efficiency in this kind of platforms can be achieved only via a synergistic hardware and software approach. In fact, while System-on-Chips are more and more programmable thus providing functional flexibility, hardwareonly power reduction techniques cannot maintain consumption under acceptable bounds. It is well understood both in research and industry that system configuration andmanagement cannot be controlled efficiently only relying on low-level firmware and hardware drivers. In fact, at this level there is lack of information about user application activity and consequently about the impact of power management decision on QoS. Even though operating system support and integration is a requirement for effective performance and energy management, more effective and QoSsensitive power management is possible if power awareness and hardware configuration control strategies are tightly integratedwith domain-specificmiddleware services. The main objective of this PhD research has been the exploration and the integration of amiddleware-centric energymanagement with applications and operating-system. We choose to focus on the CPU-memory and the video subsystems, since they are the most power-hungry components of an embedded system. A second main objective has been the definition and implementation of software facilities (like toolkits, API, and run-time engines) in order to improve programmability and performance efficiency of such platforms. Enhancing energy efficiency and programmability ofmodernMulti-Processor System-on-Chips (MPSoCs) Consumer applications are characterized by tight time-to-market constraints and extreme cost sensitivity. The software that runs on modern embedded systems must be high performance, real time, and even more important low power. Although much progress has been made on these problems, much remains to be done. Multi-processor System-on-Chip (MPSoC) are increasingly popular platforms for high performance embedded applications. This leads to interesting challenges in software development since efficient software development is a major issue for MPSoc designers. An important step in deploying applications on multiprocessors is to allocate and schedule concurrent tasks to the processing and communication resources of the platform. The problem of allocating and scheduling precedenceconstrained tasks on processors in a distributed real-time system is NP-hard. There is a clear need for deployment technology that addresses thesemulti processing issues. This problem can be tackled by means of specific middleware which takes care of allocating and scheduling tasks on the different processing elements and which tries also to optimize the power consumption of the entire multiprocessor platform. This dissertation is an attempt to develop insight into efficient, flexible and optimalmethods for allocating and scheduling concurrent applications tomultiprocessor architectures. It is a well-known problem in literature: this kind of optimization problems are very complex even in much simplified variants, therefore most authors propose simplified models and heuristic approaches to solve it in reasonable time. Model simplification is often achieved by abstracting away platform implementation ”details”. As a result, optimization problems become more tractable, even reaching polynomial time complexity. Unfortunately, this approach creates an abstraction gap between the optimization model and the real HW-SW platform. The main issue with heuristic or, more in general, with incomplete search is that they introduce an optimality gap of unknown size. They provide very limited or no information on the distance between the best computed solution and the optimal one. The goal of this work is to address both abstraction and optimality gaps, formulating accurate models which accounts for a number of ”non-idealities” in real-life hardware platforms, developing novel mapping algorithms that deterministically find optimal solutions, and implementing software infrastructures required by developers to deploy applications for the targetMPSoC platforms. Energy Efficient LCDBacklightAutoregulation on Real-LifeMultimediaAp- plication Processor Despite the ever increasing advances in Liquid Crystal Display’s (LCD) technology, their power consumption is still one of the major limitations to the battery life of mobile appliances such as smart phones, portable media players, gaming and navigation devices. There is a clear trend towards the increase of LCD size to exploit the multimedia capabilities of portable devices that can receive and render high definition video and pictures. Multimedia applications running on these devices require LCD screen sizes of 2.2 to 3.5 inches andmore to display video sequences and pictures with the required quality. LCD power consumption is dependent on the backlight and pixel matrix driving circuits and is typically proportional to the panel area. As a result, the contribution is also likely to be considerable in future mobile appliances. To address this issue, companies are proposing low power technologies suitable for mobile applications supporting low power states and image control techniques. On the research side, several power saving schemes and algorithms can be found in literature. Some of them exploit software-only techniques to change the image content to reduce the power associated with the crystal polarization, some others are aimed at decreasing the backlight level while compensating the luminance reduction by compensating the user perceived quality degradation using pixel-by-pixel image processing algorithms. The major limitation of these techniques is that they rely on the CPU to perform pixel-based manipulations and their impact on CPU utilization and power consumption has not been assessed. This PhDdissertation shows an alternative approach that exploits in a smart and efficient way the hardware image processing unit almost integrated in every current multimedia application processors to implement a hardware assisted image compensation that allows dynamic scaling of the backlight with a negligible impact on QoS. The proposed approach overcomes CPU-intensive techniques by saving system power without requiring either a dedicated display technology or hardware modification. Thesis Overview The remainder of the thesis is organized as follows. The first part is focused on enhancing energy efficiency and programmability of modern Multi-Processor System-on-Chips (MPSoCs). Chapter 2 gives an overview about architectural trends in embedded systems, illustrating the principal features of new technologies and the key challenges still open. Chapter 3 presents a QoS-driven methodology for optimal allocation and frequency selection for MPSoCs. The methodology is based on functional simulation and full system power estimation. Chapter 4 targets allocation and scheduling of pipelined stream-oriented applications on top of distributed memory architectures with messaging support. We tackled the complexity of the problem by means of decomposition and no-good generation, and prove the increased computational efficiency of this approach with respect to traditional ones. Chapter 5 presents a cooperative framework to solve the allocation, scheduling and voltage/frequency selection problem to optimality for energyefficient MPSoCs, while in Chapter 6 applications with conditional task graph are taken into account. Finally Chapter 7 proposes a complete framework, called Cellflow, to help programmers in efficient software implementation on a real architecture, the Cell Broadband Engine processor. The second part is focused on energy efficient software techniques for LCD displays. Chapter 8 gives an overview about portable device display technologies, illustrating the principal features of LCD video systems and the key challenges still open. Chapter 9 shows several energy efficient software techniques present in literature, while Chapter 10 illustrates in details our method for saving significant power in an LCD panel. Finally, conclusions are drawn, reporting the main research contributions that have been discussed throughout this dissertation.

Networks, Uncertainty, Applications and a Crusade for Optimality

In this thesis we address a collection of Network Design problems which are strongly motivated by applications from Telecommunications, Logistics and Bioinformatics. In most cases we justify the need of taking into account uncertainty in some of the problem parameters, and different Robust optimization models are used to hedge against it. Mixed integer linear programming formulations along with sophisticated algorithmic frameworks are designed, implemented and rigorously assessed for the majority of the studied problems. The obtained results yield the following observations: (i) relevant real problems can be effectively represented as (discrete) optimization problems within the framework of network design; (ii) uncertainty can be appropriately incorporated into the decision process if a suitable robust optimization model is considered; (iii) optimal, or nearly optimal, solutions can be obtained for large instances if a tailored algorithm, that exploits the structure of the problem, is designed; (iv) a systematic and rigorous experimental analysis allows to understand both, the characteristics of the obtained (robust) solutions and the behavior of the proposed algorithm.

Use of hydroclimatic forecasts for improved water management in central Texas

Accurate seasonal to interannual streamflow forecasts based on climate information are critical for optimal management and operation of water resources systems. Considering most water supply systems are multipurpose, operating these systems to meet increasing demand under the growing stresses of climate variability and climate change, population and economic growth, and environmental concerns could be very challenging. This study was to investigate improvement in water resources systems management through the use of seasonal climate forecasts. Hydrological persistence (streamflow and precipitation) and large-scale recurrent oceanic-atmospheric patterns such as the El Niño/Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), the Atlantic Multidecadal Oscillation (AMO), the Pacific North American (PNA), and customized sea surface temperature (SST) indices were investigated for their potential to improve streamflow forecast accuracy and increase forecast lead-time in a river basin in central Texas. First, an ordinal polytomous logistic regression approach is proposed as a means of incorporating multiple predictor variables into a probabilistic forecast model. Forecast performance is assessed through a cross-validation procedure, using distributions-oriented metrics, and implications for decision making are discussed. Results indicate that, of the predictors evaluated, only hydrologic persistence and Pacific Ocean sea surface temperature patterns associated with ENSO and PDO provide forecasts which are statistically better than climatology. Secondly, a class of data mining techniques, known as tree-structured models, is investigated to address the nonlinear dynamics of climate teleconnections and screen promising probabilistic streamflow forecast models for river-reservoir systems. Results show that the tree-structured models can effectively capture the nonlinear features hidden in the data. Skill scores of probabilistic forecasts generated by both classification trees and logistic regression trees indicate that seasonal inflows throughout the system can be predicted with sufficient accuracy to improve water management, especially in the winter and spring seasons in central Texas. Lastly, a simplified two-stage stochastic economic-optimization model was proposed to investigate improvement in water use efficiency and the potential value of using seasonal forecasts, under the assumption of optimal decision making under uncertainty. Model results demonstrate that incorporating the probabilistic inflow forecasts into the optimization model can provide a significant improvement in seasonal water contract benefits over climatology, with lower average deficits (increased reliability) for a given average contract amount, or improved mean contract benefits for a given level of reliability compared to climatology. The results also illustrate the trade-off between the expected contract amount and reliability, i.e., larger contracts can be signed at greater risk.

Analysis of spring break-up and its effects on a biomass feedstock supply chain in northern Michigan

Demand for bio-fuels is expected to increase, due to rising prices of fossil fuels and concerns over greenhouse gas emissions and energy security. The overall cost of biomass energy generation is primarily related to biomass harvesting activity, transportation, and storage. With a commercial-scale cellulosic ethanol processing facility in Kinross Township of Chippewa County, Michigan about to be built, models including a simulation model and an optimization model have been developed to provide decision support for the facility. Both models track cost, emissions and energy consumption. While the optimization model provides guidance for a long-term strategic plan, the simulation model aims to present detailed output for specified operational scenarios over an annual period. Most importantly, the simulation model considers the uncertainty of spring break-up timing, i.e., seasonal road restrictions. Spring break-up timing is important because it will impact the feasibility of harvesting activity and the time duration of transportation restrictions, which significantly changes the availability of feedstock for the processing facility. This thesis focuses on the statistical model of spring break-up used in the simulation model. Spring break-up timing depends on various factors, including temperature, road conditions and soil type, as well as individual decision making processes at the county level. The spring break-up model, based on the historical spring break-up data from 27 counties over the period of 2002-2010, starts by specifying the probability distribution of a particular county’s spring break-up start day and end day, and then relates the spring break-up timing of the other counties in the harvesting zone to the first county. In order to estimate the dependence relationship between counties, regression analyses, including standard linear regression and reduced major axis regression, are conducted. Using realizations (scenarios) of spring break-up generated by the statistical spring breakup model, the simulation model is able to probabilistically evaluate different harvesting and transportation plans to help the bio-fuel facility select the most effective strategy. For early spring break-up, which usually indicates a longer than average break-up period, more log storage is required, total cost increases, and the probability of plant closure increases. The risk of plant closure may be partially offset through increased use of rail transportation, which is not subject to spring break-up restrictions. However, rail availability and rail yard storage may then become limiting factors in the supply chain. Rail use will impact total cost, energy consumption, system-wide CO2 emissions, and the reliability of providing feedstock to the bio-fuel processing facility.

The coevolution of cooperation and dispersal in social groups and its implications for the emergence of multicellularity

Background Recent work on the complexity of life highlights the roles played by evolutionary forces at different levels of individuality. One of the central puzzles in explaining transitions in individuality for entities ranging from complex cells, to multicellular organisms and societies, is how different autonomous units relinquish control over their functions to others in the group. In addition to the necessity of reducing conflict over effecting specialized tasks, differentiating groups must control the exploitation of the commons, or else be out-competed by more fit groups. Results We propose that two forms of conflict – access to resources within groups and representation in germ line – may be resolved in tandem through individual and group-level selective effects. Specifically, we employ an optimization model to show the conditions under which different within-group social behaviors (cooperators producing a public good or cheaters exploiting the public good) may be selected to disperse, thereby not affecting the commons and functioning as germ line. We find that partial or complete dispersal specialization of cheaters is a general outcome. The propensity for cheaters to disperse is highest with intermediate benefit:cost ratios of cooperative acts and with high relatedness. An examination of a range of real biological systems tends to support our theory, although additional study is required to provide robust tests. Conclusion We suggest that trait linkage between dispersal and cheating should be operative regardless of whether groups ever achieve higher levels of individuality, because individual selection will always tend to increase exploitation, and stronger group structure will tend to increase overall cooperation through kin selected benefits. Cheater specialization as dispersers offers simultaneous solutions to the evolution of cooperation in social groups and the origin of specialization of germ and soma in multicellular organisms.

Contribution of re-regulation reservoirs considering pumping capability to environmentally friendly hydropower operation

Environmental constraints imposed on hydropoweroperation are usually given in the form of minimum environmental flows and maximum and minimum rates of change of flows, or ramp rates. One solution proposed to mitigate the environmental impact caused by the flows discharged by a hydropower plant while reducing the economic impact of the above-mentioned constraints consists in building a re-regulationreservoir, or afterbay, downstream of the power plant. Adding pumpingcapability between the re-regulationreservoir and the main one could contribute both to reducing the size of the re-regulationreservoir, with the consequent environmental improvement, and to improving the economic feasibility of the project, always fulfilling the environmental constraints imposed to hydropoweroperation. The objective of this paper is studying the contribution of a re-regulationreservoir to fulfilling the environmental constraints while reducing the economic impact of said constraints. For that purpose, a revenue-driven optimization model based on mixed integer linear programming is used. Additionally, the advantages of adding pumpingcapability are analysed. In order to illustrate the applicability of the methodology, a case study based on a real hydropower plant is presented

Balancing groundwater conservation and rural livelihoods under water and climate uncertainties: An integrated hydro-economic modeling framework

In arid countries worldwide, social conflicts between irrigation-based human development and the conservation of aquatic ecosystems are widespread and attract many public debates. This research focuses on the analysis of water and agricultural policies aimed at conserving groundwater resources and maintaining rurallivelihoods in a basin in Spain's central arid region. Intensive groundwater mining for irrigation has caused overexploitation of the basin's large aquifer, the degradation of reputed wetlands and has given rise to notable social conflicts over the years. With the aim of tackling the multifaceted socio-ecological interactions of complex water systems, the methodology used in this study consists in a novel integration into a common platform of an economic optimization model and a hydrology model WEAP (Water Evaluation And Planning system). This robust tool is used to analyze the spatial and temporal effects of different water and agricultural policies under different climate scenarios. It permits the prediction of different climate and policy outcomes across farm types (water stress impacts and adaptation), at basin's level (aquifer recovery), and along the policies’ implementation horizon (short and long run). Results show that the region's current quota-based water policies may contribute to reduce water consumption in the farms but will not be able to recover the aquifer and will inflict income losses to the rural communities. This situation would worsen in case of drought. Economies of scale and technology are evidenced as larger farms with cropping diversification and those equipped with modern irrigation will better adapt to water stress conditions. However, the long-term sustainability of the aquifer and the maintenance of rurallivelihoods will be attained only if additional policy measures are put in place such as the control of illegal abstractions and the establishing of a water bank. Within the policy domain, the research contributes to the new sustainable development strategy of the EU by concluding that, in water-scarce regions, effective integration of water and agricultural policies is essential for achieving the water protection objectives of the EU policies. Therefore, the design and enforcement of well-balanced region-specific polices is a major task faced by policy makers for achieving successful water management that will ensure nature protection and human development at tolerable social costs. From a methodological perspective, this research initiative contributes to better address hydrological questions as well as economic and social issues in complex water and human systems. Its integrated vision provides a valuable illustration to inform water policy and management decisions within contexts of water-related conflicts worldwide.

Propuesta metodológica para la identificación de medidas de adaptación al cambio climático en sistemas de recursos hídricos

Esta tesis realiza una contribución metodológica en el estudio de medidas de adaptación potencialmente adecuadas a largo plazo, donde los sistemas de recursos hídricos experimentan fuertes presiones debido a los efectos del cambio climático. Esta metodología integra el análisis físico del sistema, basándose en el uso de indicadores que valoran el comportamiento de éste, y el análisis económico mediante el uso del valor del agua. El procedimiento metodológico inicia con la construcción de un conjunto de escenarios futuros, que capturan por un lado las características de variabilidad de las aportaciones de diversos modelos climáticos y, por otro, las características hidrológicas de la zona de estudio. Las zonas de estudio seleccionadas fueron las cuencas del Guadalquivir, Duero y Ebro y se utilizaron como datos observados las series de escorrentía en régimen natural estimadas por el modelo SIMPA que está calibrado en la totalidad del territorio español. Estas series observadas corresponden al periodo 1961-1990. Los escenarios futuros construidos representan el periodo 2071-2100. La identificación de medidas de adaptación se apoyó en el uso de indicadores que sean capaces de caracterizar el comportamiento de un sistema de recursos hídricos frente a los efectos del cambio climático. Para ello se seleccionaron los indicadores de calidad de servicio (I1) y de confiabilidad de la demanda (I2) propuestos por Martin-Carrasco et al. (2012). Estos indicadores valoran el comportamiento de un sistema mediante la identificación de los problemas de escasez de agua que presente, y requieren para su cuantificación el uso de un modelo de optimización. Para este estudio se ha trabajado con el modelo de optimización OPTIGES. La determinación de estos indicadores fue realizada para análisis a corto plazo donde los efectos del cambio climático no son de relevancia, por lo que fue necesario analizar su capacidad para ser usados en sistemas afectados por dichos efectos. Para este análisis se seleccionaron tres cuencas españolas: Guadalquivir, Duero y Ebro, determinándose que I2 no es adecuado para este tipo de escenarios. Por ello se propuso un nuevo indicador “Indicador de calidad de servicio bajo cambio climático” (I2p) que mantiene los mismos criterios de valoración que I2 pero que responde mejor bajo fuertes reducciones de aportaciones producto del cambio climático. La metodología propuesta para la identificación de medidas de adaptación se basa en un proceso iterativo en el cual se van afectando diversos elementos que conforman el esquema del sistema bajo acciones de gestión previamente identificadas, hasta llegar a un comportamiento óptimo dado por el gestor. Las mejoras de estas afectaciones son cuantificadas mediante los indicadores I1 e I2p, y de este conjunto de valores se selecciona la que se acerca más al comportamiento óptimo. Debido a la extensa cantidad de información manejada en este análisis, se desarrolló una herramienta de cálculo automatizada en Matlab. El proceso seguido por esta herramienta es: (i) Ejecución del modelo OPTIGES para las diferentes modificaciones por acciones de gestión; (ii) Cálculo de los valores de I1 e I2p para cada una de estas afectaciones; y (iii) Selección de la mejor opción. Este proceso se repite hasta llegar al comportamiento óptimo buscado, permitiendo la identificación de las medidas de adaptación mas adecuadas. La aplicación de la metodología para la identificación de medidas de adaptación se realizó en la cuenca del Guadalquivir, por ser de las tres cuencas analizadas bajo los indicadores I1 e I2p la que presenta los problemas más serios de escasez de agua. Para la identificación de medidas de adaptación se analizaron dos acciones de gestión: 1) incremento de los volúmenes de regulación y 2) reducción de las demandas de riego, primero bajo la valoración del comportamiento físico del sistema (análisis de sensibilidad) permitiendo identificar que la primera acción de gestión no genera cambios importantes en el comportamiento del sistema, que si se presentan bajo la segunda acción. Posteriormente, con la acción que genera cambios importantes en el comportamiento del sistema (segunda acción) se identificaron las medidas de adaptación más adecuadas, mediante el análisis físico y económico del sistema. Se concluyó que en la cuenca del Guadalquivir, la acción de reducción de las demandas de riego permite minimizar e incluso eliminar los problemas de escasez de agua que se presentarían a futuro bajo diferentes proyecciones hidrológicas, aunque estas mejoras implicarían fuertes reducciones en dichas demandas. Siendo las demandas más afectadas aquellas ubicadas en cabecera de cuenca. Los criterios para la reducción de las demandas se encuentran en función de las productividades y garantías con las que son atendidas dichas demandas. This thesis makes a methodological contribution to the study of potentially suitable adaptation measures in the long term, where water resource systems undergo strong pressure due to the effects of climate change. This methodology integrates the physical analysis of the system, by the use of indicators which assess its behavior, and the economic analysis by the use of the value of water. The methodological procedure begins with the building of a set of future scenarios that capture, by one hand, the characteristics and variability of the streamflow of various climate models and, on the other hand, the hydrological characteristics of the study area. The study areas chosen were the Guadalquivir, Ebro and Duero basins, and as observed data where used runoff series in natural regimen estimated by the SIMPA model, which is calibrated in the whole Spanish territory. The observed series are for the 1961-1990 period. The future scenarios built represent the 2071-2100 periods. The identification of adaptation measures relied on the use of indicators that were able of characterize the behavior of one water resource system facing the effects of climate change. Because of that, the Demand Satisfaction Index (I1) and the Demand Reliability Index (I2) proposed by Martin-Carrasco et al. (2012) were selected. These indicators assess the behavior of a system by identifying the water scarcity problems that it presents, and require in order to be quantified the use of one optimization model. For this study the OPTIGES optimization model has been used. The determination of the indicators was made for the short-term analysis where the climates change effect are not relevant, so it was necessary to analyze their capability to be used in systems affected by those these. For this analysis three Spanish basins were selected: Guadalquivir, Duero and Ebro. It was determined that the indicator I2 is not suitable for this type of scenario. It was proposed a new indicator called “Demand Reliability Index under climate change” (I2p), which keeps the same assessment criteria than I2, but responsive under heavy reductions of streamflow due to climate change. The proposed methodology for identifying adaptation measures is based on an iterative process, in which the different elements of the system´s schema are affected by previously defined management actions, until reach an optimal behavior given by the manager. The improvements of affectations are measured by indicators I1 e I2p, and from this set of values it is selected the affectation that is closer to the optimal behavior. Due to the large amount of information managed in this analysis, it was developed an automatic calculation tool in Matlab. The process followed by this tool is: Firstly, it executes the OPTIGES model for the different modifications by management actions; secondly, it calculates the values of I1 e I2p for each of these affectations; and finally it chooses the best option. This process is performed for the different iterations that are required until reach the optimal behavior, allowing to identify the most appropriate adaptation measured. The application of the methodology for the identification of adaptation measures was conducted in the Guadalquivir basin, due to this was from the three basins analyzed under the indicators I1 e I2p, which presents the most serious problems of water scarcity. For the identification of adaptation measures there were analyzed two management actions: 1) To increase the regulation volumes, and 2) to reduce the irrigation demands, first under the assessment of the physical behavior of the system (sensibility analysis), allowing to identify that the first management action does not generate significant changes in the system´s behavior, which there are present under the second management action. Afterwards, with the management action that generates significant changes in the system´s behavior (second management action), there were identified the most adequate adaptation measures, through the physical and economic analysis of the system. It was concluded that in the Guadalquivir basin, the action of reduction of irrigation demands allows to minimize or even eliminate the water scarcity problems that could exist in the future under different hydrologic projections, although this improvements should involve strong reductions of the irrigation demands. Being the most affected demands those located in basins head. The criteria for reducing the demands are based on the productivities and reliabilities with which such demands are meet.

Airport Taxi Planning: Lagrangian Decomposition

The airport taxi planning (TP) module is a decision tool intended to guide airport surface management operations. TP is defined by a flow network optimization model that represents flight ground movements and improves aircraft taxiing routes and schedules during periods of aircraft congestion. TP is not intended to operate as a stand‐alone tool for airport operations management: on the contrary, it must be used in conjunction with existing departing and arriving traffic tools and overseen by the taxi planner of the airport, also known as the aircraft ground controller. TP must be flexible in order to accommodate changing inputs while maintaining consistent routes and schedules already delivered from past executions. Within this dynamic environment, the execution time of TP may not exceed a few minutes. Classic methods for solving binary multi‐commodity flow networks with side constraints are not efficient enough; therefore, a Lagrangian decomposition methodology has been adapted to solve it. We demonstrate TP Lagrangian decomposition using actual data from the Madrid‐Barajas Airport

Robustness and Recoverability in Transport Logistics

Abstract Transport is the foundation of any economy: it boosts economic growth, creates wealth, enhances trade, geographical accessibility and the mobility of people. Transport is also a key ingredient for a high quality of life, making places accessible and bringing people together. The future prosperity of our world will depend on the ability of all of its regions to remain fully and competitively integrated in the world economy. Efficient transport is vital in making this happen. Operations research can help in efficiently planning the design and operating transport systems. Planning and operational processes are fields that are rich in combinatorial optimization problems. These problems can be analyzed and solved through the application of mathematical models and optimization techniques, which may lead to an improvement in the performance of the transport system, as well as to a reduction in the time required for solving these problems. The latter aspect is important, because it increases the flexibility of the system: the system can adapt in a faster way to changes in the environment (i.e.: weather conditions, crew illness, failures, etc.). These disturbing changes (called disruptions) often enforce the schedule to be adapted. The direct consequences are delays and cancellations, implying many schedule adjustments and huge costs. Consequently, robust schedules and recovery plans must be developed in order to fight against disruptions. This dissertation makes contributions to two different fields: rail and air applications. Robust planning and recovery methods are presented. In the field of railway transport we develop several mathematical models which answer to RENFE’s (the major railway operator in Spain) needs: 1. We study the rolling stock assignment problem: here, we introduce some robust aspects in order to ameliorate some operations which are likely to fail. Once the rolling stock assignment is known, we propose a robust routing model which aims at identifying the train units’ sequences while minimizing the expected delays and human resources needed to perform the sequences. 2. It is widely accepted that the sequential solving approach produces solutions that are not global optima. Therefore, we develop an integrated and robust model to determine the train schedule and rolling stock assignment. We also propose an integrated model to study the rolling stock circulations. Circulations are determined by the rolling stock assignment and routing of the train units. 3. Although our aim is to develop robust plans, disruptions will be likely to occur and recovery methods will be needed. Therefore, we propose a recovery method which aims to recover the train schedule and rolling stock assignment in an integrated fashion all while considering the passenger demand. In the field of air transport we develop several mathematical models which answer to IBERIA’s (the major airline in Spain) needs: 1. We look at the airline-scheduling problem and develop an integrated approach that optimizes schedule design, fleet assignment and passenger use so as to reduce costs and create fewer incompatibilities between decisions. Robust itineraries are created to ameliorate misconnected passengers. 2. Air transport operators are continuously facing competition from other air operators and different modes of transport (e.g., High Speed Rail). Consequently, airline profitability is critically influenced by the airline’s ability to estimate passenger demands and construct profitable flight schedules. We consider multi-modal competition including airline and rail, and develop a new approach that estimates the demand associated with a given schedule; and generates airline schedules and fleet assignments using an integrated schedule design and fleet assignment optimization model that captures the impacts of schedule decisions on passenger demand.