In the last two decades, the western world witnessed a continuous rise in the health expenditure. Meanwhile, complaints from patients on excessive waiting times are also increasing. In the past, many researchers have tried to devise appointment scheduling rules to provide trade-offs between maximizing patients’ satisfaction and minimizing the costs of the health providers. For instance, this challenge appears appointment scheduling problems (ASP). Commonly used methods in ASP include analytical methods, simulation studies, and combination of simulation with heuristic approaches. Analytical methods (e.g., queuing theory and mathematical programming) face challenges of fully capturing the complexities of systems and usually make strong assumptions for tractability of problems. These methods simplify the whole system to a single-stage unit and ignore the actual system factors such as the presence of multiple stages and/or resource constraints. Simulation studies, conversely, are able to model most complexities of the actual system, but they typically lack an optimization strategy to deliver optimal appointment schedules. Also, heuristic approaches normally are based on intuitive rules and do not perform well as standalone methods. In order to reach an optimal schedule while considering complexities in actual health care systems, this thesis proposes efficient and effective methods that yield (near) optimal appointment schedules by integrating mathematical programming, a tabu search optimization algorithm and discrete event simulation. The proposed methodologies address the challenges and complexities of scheduling in real world multistage healthcare units in the presence of stochastic service durations, a mix of patient types, patients with heterogeneous service sequence, and resource constraints. Moreover, the proposed methodology is capable of finding the optimum considering simultaneously multiple performance criteria. A Pareto front (a set of optimal solutions) for the performance criteria can be obtained using the proposed methods. Healthcare management can use the Pareto front to choose the appropriate policy based on different conditions and priorities. In addition, the proposed method has been applied to two case studies of Operating Rooms departments in two major Canadian hospitals. The comparison of actual schedules and the ones yielded by the proposed method indicates that proposed method can improve the appointment scheduling in realistic clinical settings.

In the last two decades, the western world witnessed a continuous rise in the health expenditure. Meanwhile, complaints from patients on excessive waiting times are also increasing. In the past, many researchers have tried to devise appointment scheduling rules to provide trade-offs between maximizing patients’ satisfaction and minimizing the costs of the health providers. For instance, this challenge appears appointment scheduling problems (ASP). Commonly used methods in ASP include analytical methods, simulation studies, and combination of simulation with heuristic approaches. Analytical methods (e.g., queuing theory and mathematical programming) face challenges of fully capturing the complexities of systems and usually make strong assumptions for tractability of problems. These methods simplify the whole system to a single-stage unit and ignore the actual system factors such as the presence of multiple stages and/or resource constraints. Simulation studies, conversely, are able to model most complexities of the actual system, but they typically lack an optimization strategy to deliver optimal appointment schedules. Also, heuristic approaches normally are based on intuitive rules and do not perform well as standalone methods. In order to reach an optimal schedule while considering complexities in actual health care systems, this thesis proposes efficient and effective methods that yield (near) optimal appointment schedules by integrating mathematical programming, a tabu search optimization algorithm and discrete event simulation. The proposed methodologies address the challenges and complexities of scheduling in real world multistage healthcare units in the presence of stochastic service durations, a mix of patient types, patients with heterogeneous service sequence, and resource constraints. Moreover, the proposed methodology is capable of finding the optimum considering simultaneously multiple performance criteria. A Pareto front (a set of optimal solutions) for the performance criteria can be obtained using the proposed methods. Healthcare management can use the Pareto front to choose the appropriate policy based on different conditions and priorities. In addition, the proposed method has been applied to two case studies of Operating Rooms departments in two major Canadian hospitals. The comparison of actual schedules and the ones yielded by the proposed method indicates that proposed method can improve the appointment scheduling in realistic clinical settings.

Thesis (M.E.E.)--University of Delaware, 2007. / Principal faculty advisor: Dennis W. Prather, Dept. of Electrical & Computer Engineering. Includes bibliographical references.

Renewable energy (RE) resources are relatively unpredictable and dependent on climatic conditions. The negative effects of existing randomness in RE resources can be reduced by the integration of RE resources into what is called Hybrid Renewable Energy Systems (HRES). The design of HRES remains as a complicated problem since there is uncertainty in energy prices, demand, and RE sources. In addition, it is a multi-objective design since several conflicting objectives must be considered. In this thesis, an optimal sizing approach has been proposed to aid decision makers in sizing and performance analysis of this kind of energy supply systems. First, a straightforward methodology based on ε-constraint method is proposed for optimal sizing of HRESs containing RE power generators and two storage devices. The ε-constraint method has been applied to minimize simultaneously the total net present cost of the system, unmet load, and fuel emission. A simulation-based particle swarm optimization approach has been used to tackle the multi-objective optimization problem. In the next step, a Pareto-based search technique, named dynamic multi-objective particle swarm optimization, has been performed to improve the quality of the Pareto front (PF) approximated by the ε-constraint method. The proposed method is examined for a case study including wind turbines, photovoltaic panels, diesel generators, batteries, fuel cells, electrolyzers, and hydrogen tanks. Well-known metrics from the literature are used to evaluate the generated PF. Afterward, a multi-objective approach is presented to consider the economic, reliability and environmental issues at various renewable energy ratio values when optimizing the design of building energy supply systems. An existing commercial apartment building operating in a cold Canadian climate has been described to apply the proposed model. In this test application, the model investigates the potential use of RE resources for the building. Furthermore, the application of plug-in electric vehicles instead of gasoline car for transportation is studied. Comparing model results against two well-known reported multi-objective algorithms has also been examined. Finally, the existing uncertainties in RE and load are explicitly incorporated into the model to give more accurate and realistic results. An innovative and easy to implement stochastic multi-objective approach is introduced for optimal sizing of an HRES. / February 2016

Real-time sensing brings the proliferation of big data that contains rich information of complex systems. It is well known that real-world systems show high levels of nonlinear and nonstationary behaviors in the presence of extraneous noise. This brings significant challenges for human experts to visually inspect the integrity and performance of complex systems from the collected data. My research goal is to develop innovative methodologies for modeling and optimizing complex systems, and create enabling technologies for real-world applications. Specifically, my research focuses on Mining Dynamic Recurrences in Nonlinear and Nonstationary Systems for Feature Extraction, Process Monitoring and Fault Diagnosis. This research will enable and assist in (i) sensor-driven modeling, monitoring and optimization of complex systems; (ii) integrating product design with system design of nonlinear dynamic processes; and (iii) creating better prediction/diagnostic tools for real-world complex processes. My research accomplishments include the following. (1) Feature Extraction and Analysis: I proposed a novel multiscale recurrence analysis to not only delineate recurrence dynamics in complex systems, but also resolve the computational issues for the large-scale datasets. It was utilized to identify heart failure subjects from the 24-hour heart rate variability (HRV) time series and control the quality of mobile-phone-based electrocardiogram (ECG) signals. (2) Modeling and Prediction: I proposed the design of stochastic sensor network to allow a subset of sensors at varying locations within the network to transmit dynamic information intermittently, and a new approach of sparse particle filtering to model spatiotemporal dynamics of big data in the stochastic sensor network. It may be noted that the proposed algorithm is very general and can be potentially applicable for stochastic sensor networks in a variety of disciplines, e.g., environmental sensor network and battlefield surveillance network. (3) Monitoring and Control: Process monitoring of dynamic transitions in complex systems is more concerned with aperiodic recurrences and heterogeneous types of recurrence variations. However, traditional recurrence analysis treats all recurrence states homogeneously, thereby failing to delineate heterogeneous recurrence patterns. I developed a new approach of heterogeneous recurrence analysis for complex systems informatics, process monitoring and anomaly detection. (4) Simulation and Optimization: Another research focuses on fractal-based simulation to study spatiotemporal dynamics on fractal surfaces of high-dimensional complex systems, and further optimize spatiotemporal patterns. This proposed algorithm is applied to study the reaction-diffusion modeling on fractal surfaces and real-world 3D heart surfaces.

Water is an essential resource from an environmental, biological, economic or social point of view. In basin management, the irregular distribution in time and in space of this resource is well known. This issue is worsened by extreme climate conditions, generating drought periods or flood events. For both situations, optimal management is necessary. In one case, different water uses should be supplied efficiently using the available surface and groundwater resources. In another case, the most important goal is to avoid damages in flood areas, including the loss of human lives, but also to optimize the revenue of energy production in hydropower plants, or in other uses. The approach presented in this thesis proposes to obtain optimal management rules in water resource systems. With this aim, evolutionary algorithms were combined with simulation models. The first ones, as optimization tools, are responsible for guiding the process iterations. In each iteration, a new management rule is defined in the simulation model, which is computed to comprehend the situation of the system after applying this new management. For testing the proposed methodology, four evolutionary algorithms were assessed combining them with two simulation models. The methodology was implemented in four real case studies. This thesis is presented as a compendium of five manuscripts: three scientific papers published in journals (which are indexed in the Journal Citation Report), another under review, and the last manuscript from Conference Proceedings. In the first manuscript, the Pikaia optimization algorithm was combined with the network flow SIMGES simulation model for obtaining four different types of optimal management rules in the Júcar River Basin. In addition, the parameters of the Pikaia algorithm were also analyzed to identify the best combination of them to use in the optimization process. In the second scientific paper, the multi-objective NSGA-II algorithm was assessed to obtain a parametric management rule in the Mijares River basin. In this case, the same simulation model was linked with the evolutionary algorithm. In the Conference manuscript, an in-depth analysis of the Tirso-Flumendosa-Campidano (TFM) system using different scenarios and comparing three water simulation models for water resources management was developed. The third published manuscript presented the assessment and comparison of two evolutionary algorithms for obtaining optimal rules in the TFM system using SIMGES model. The algorithms assessed were the SCE-UA and the Scatter Search. In this research paper, the parameters of both algorithms were also analyzed as it was done with the Pikaia algorithm. The management rules in the three first manuscripts were focused to avoid or minimize deficits in urban and agrarian demands and, in some case studies, also to minimize the water pumped. Finally, in the last document, two of the algorithms used in previous manuscripts were assessed, the mono-objective SCE-UA and the multi-objective NSGA-II. For this research, the algorithms were combined with RS MINERVE software to manage flood events in Visp River basin minimizing damages in risk areas and losses in hydropower plants. Results reached in the five manuscripts demonstrate the validity of the approach. In all the case studies and with the different evolutionary algorithms assessed, the obtained management rules achieved a better system management than the base scenario of each case. These results usually mean a decrease of the economic costs in the management of water resources. However, comparing the four algorithms assessed, SCE-UA algorithm proved to be the most efficient due to the different stop/convergence criteria and its formulation. Nevertheless, NSGA-II is the most recommended due to its multi-objective search focus on the enhancement of different objectives with the same importance where the decision makers can make the best decision for the management of the system. / El agua es un recurso esencial desde el punto de vista ambiental, biológico, económico o social. En la gestión de cuencas, es bien conocido que la distribución del recurso en el tiempo y el espacio es irregular. Este problema se agrava debido a condiciones climáticas extremas, generando períodos de sequía o inundaciones. Para ambas situaciones, una gestión óptima es necesaria. En un caso, el suministro de agua a los diferentes usos del sistema debe realizarte eficientemente empleando los recursos disponibles, tanto superficiales como subterráneos. En el otro caso, el objetivo más importante es evitar daños en las zonas de inundación, incluyendo la pérdida de vidas humanas, pero al mismo tiempo, optimizar los beneficios de centrales hidroeléctricas, o de otros usos. El enfoque presentado en esta tesis propone la obtención de reglas de gestión óptimas en sistemas reales de recursos hídricos. Con este objetivo, se combinaron algoritmos evolutivos con modelos de simulación. Los primeros, como herramientas de optimización, encargados de guiar las iteraciones del proceso. En cada iteración se define una nueva regla de gestión en el modelo de simulación, que se evalúa para conocer la situación del sistema después de aplicar esta nueva gestión. Para probar la metodología propuesta, se evaluaron cuatro algoritmos evolutivos combinándolos con dos modelos de simulación. La metodología se implementó en cuatro casos de estudio reales. Esta tesis se presenta como un compendio de cinco publicaciones: tres de ellas en revistas indexadas en el Journal Citation Report, otra en revisión y la última como publicación de un congreso. En el primer manuscrito, el algoritmo de optimización Pikaia se combinó con el modelo de simulación SIMGES para obtener reglas de gestión óptimas en la cuenca del río Júcar. Además, se analizaron los parámetros del algoritmo para identificar la mejor combinación de los mismos en el proceso de optimización. El segundo artículo evaluó el algoritmo multi-objetivo NSGA-II para obtener una regla de gestión paramétrica en la cuenca del río Mijares. En el trabajo presentado en el congreso se desarrolló un análisis en profundidad del sistema Tirso-Flumendosa-Campidano utilizando diferentes escenarios y comparando tres modelos de simulación para la gestión de los recursos hídricos. En el tercer manuscrito publicado se evaluó y comparó dos algoritmos evolutivos (SCE-UA y Scatter Search) para obtener reglas de gestión óptimas en el sistema Tirso-Flumendosa-Campidano. En dicha investigación también se analizaron los parámetros de ambos algoritmos. Las reglas de gestión de estas cuatro publicaciones se enfocaron en evitar o minimizar los déficits de las demandas urbanas y agrarias y, en ciertos casos, también en minimizar el caudal bombeado, utilizando para ello el modelo de simulación SIMGES. Finalmente, en la última publicación se evaluó el algoritmo mono-objetivo SCE-UA y el multi-objetivo NSGA-II. Para esta investigación, los algoritmos se combinaron con el software RS MINERVE para gestionar los eventos de inundación en la cuenca del río Visp minimizando los daños en las zonas de riesgo y las pérdidas en las centrales hidroeléctricas. Los resultados obtenidos en las cinco publicaciones demuestran la validez del enfoque. En todos los casos de estudio y, con los diferentes algoritmos evolutivos evaluados, las reglas de gestión obtenidas lograron una mejor gestión del sistema que el escenario base de cada caso. Estos resultados suelen representar una disminución de los costes económicos en la gestión de los recursos hídricos. Comparando los cuatro algoritmos, el SCE-UA demostró ser el más eficiente debido a los diferentes criterios de convergencia. No obstante, el NSGA-II es el más recomendado debido a su búsqueda multi-objetivo enfocada en la mejora, con la misma importancia, de diferentes objetivos, donde los tomadores de decisiones pueden sel / L'aigua és un recurs essencial des del punt de vista ambiental, biològic, econòmic o social. En la gestió de conques, és ben conegut que la distribució del recurs en el temps i l'espai és irregular. Este problema s'agreuja a causa de condicions climàtiques extremes, generant períodes de sequera o inundacions. Per a ambdúes situacions, una gestió òptima és necessària. En un cas, el subministrament d'aigua als diferents usos del sistema ha de realitzar-se eficientment utilitzant els recursos disponibles, tant superficials com subterranis. En l'altre cas, l'objectiu més important és evitar danys en les zones d'inundació, incloent la pèrdua de vides humanes, però al mateix temps, optimitzar els beneficis de centrals hidroelèctriques, o d'altres usos. La proposta d'esta tesi és l'obtenció de regles de gestió òptimes en sistemes reals de recursos hídrics. Amb este objectiu, es van combinar algoritmes evolutius amb models de simulació. Els primers, com a ferramentes d'optimització, encarregats de guiar les iteracions del procés. En cada iteració es definix una nova regla de gestió en el model de simulació, que s'avalua per a conéixer la situació del sistema després d'aplicar esta nova gestió. Per a provar la metodologia proposada, es van avaluar quatre algoritmes evolutius combinant-los amb dos models de simulació. La metodologia es va implementar en quatre casos d'estudi reals. Esta tesi es presenta com un compendi de cinc publicacions: tres d'elles en revistes indexades en el Journal Citation Report, una altra en revisió i l'última com a publicació d'un congrés. En el primer manuscrit, l'algoritme d'optimització Pikaia es va combinar amb el model de simulació SIMGES per a obtindre regles de gestió òptimes en la conca del riu Xúquer. A més, es van analitzar els paràmetres de l'algoritme per a identificar la millor combinació dels mateixos en el procés d'optimització. El segon article va avaluar l'algoritme multi-objectiu NSGA-II per a obtindre una regla de gestió paramètrica en la conca del riu Millars. En el treball presentat en el congrés es va desenvolupar una anàlisi en profunditat del sistema Tirso-Flumendosa-Campidano utilitzant diferents escenaris i comparant tres models de simulació per a la gestió dels recursos hídrics. En el tercer manuscrit publicat es va avaluar i va comparar dos algoritmes evolutius (SCE-UA i Scatter Search) per a obtindre regles de gestió òptimes en el sistema Tirso-Flumendosa-Campidano. En dita investigació també es van analitzar els paràmetres d'ambdós algoritmes. Les regles de gestió d'estes quatre publicacions es van enfocar a evitar o minimitzar els dèficits de les demandes urbanes i agràries i, en certs casos, també a minimitzar el cabal bombejat, utilitzant per a això el model de simulació SIMGES. Finalment, en l'última publicació es va avaluar l'algoritme mono-objectiu SCE-UA i el multi-objetiu NSGA-II. Per a esta investigació, els algoritmes es van combinar amb el programa RS MINERVE per a gestionar els esdeveniments d'inundació en la conca del riu Visp minimitzant els danys en les zones de risc i les pèrdues en les centrals hidroelèctriques. Els resultats obtinguts en les cinc publicacions demostren la validesa de la metodología. En tots els casos d'estudi i, amb els diferents algoritmes evolutius avaluats, les regles de gestió obtingudes van aconseguir una millor gestió del sistema que l'escenari base de cada cas. Estos resultats solen representar una disminució dels costos econòmics en la gestió dels recursos hídrics. Comparant els quatre algoritmes, el SCE-UA va demostrar ser el més eficient a causa dels diferents criteris de convergència. No obstant això, el NSGA-II és el més recomanat a causa de la seua cerca multi-objectiu enfocada en la millora, amb la mateixa importància, de diferents objectius, on els decisors poden seleccionar la millor opció per a la gestió del sistema. / Lerma Elvira, N. (2017). Assessment and implementation of evolutionary algorithms for optimal management rules design in water resources systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90547 / TESIS

This thesis focuses on the storage location assignment problem using modern meta-heuristic techniques combined with realistic simulation. A graphical tool implemented as part of this work is capable of warehouse model creation, generation of synthetic customer orders, optimization of product allocation using state of the art techniques, extensive warehouse simulation, and a pathfinder capable of finding the shortest path for orders going through the system. The work presents the comparison between different approaches based on many parameters to reach the most efficient allocation of products to warehouse slots. The author conducted tests on an experimental warehouse featuring almost twice the throughput -- 57%. The benefit of this work is a possibility to create model of an already built warehouse and its simulation and optimization, driving impact on the throughput of the warehouse, saving the user's resources, or helping him in planning and bottle-neck identification. Furthermore, this thesis introduces a new approach to warehouse optimization and new optimization criteria.

No description available.

Fossil fuel is still a predominant source of the global energy requirement. Hydrotreating of whole crude oil has the ability to increase the productivity of middle distillate fractions and improve the fuel quality by simultaneously reducing contaminants such as sulfur, nitrogen, vanadium, nickel and asphaltene to the levels required by the regulatory bodies. Hydrotreating is usually carried out in a trickle bed reactor (TBR) where hydrodesulfurization (HDS), hydrodenitrogenation (HDN), hydrodemetallization (HDM) and hydrodeasphaltenization (HDAs) reactions take place simultaneously. To develop a detailed and a validated TBR process model which can be used for design and optimization of the hydrotreating process, it is essential to develop kinetic models for each of these reactions. Most recently, the authors have developed kinetic models for all of these chemical reactions except that of HDAs. In this work, a kinetic model (in terms of kinetic parameters) for the HDAs reaction in the TBR is developed. A three phase TBR process model incorporating the HDAs reactions with unknown kinetic parameters is developed. Also, a series of experiments has been conducted in an isothermal TBR under different operating conditions affecting the removal of asphaltene. The unknown kinetic parameters are then obtained by applying a parameter estimation technique based on minimization of the sum of square errors (SSEs) between the experimental and predicted concentrations of asphaltene compound in the crude oil. The full model with the estimated kinetic parameters is then applied to evaluate the removal of asphaltene (thus affecting fuel quality) under different operating conditions (than those used in experiments).

No description available.