A dissertação aborda e compara as características dos índices de Sharpe (SR) e suas variantes, SRc e SRd, Sharpe generalizado (GSR ) e potencial de investimento (IP), sendo os índices GSR e IP associados a alguma função de utilidade. Pelo fato de o GSR e o IP serem idênticos, testes empíricos foram realizados entre SRc e o GSR. Ambos foram avaliados teoricamente sob dois aspectos, o que definimos de análise retrospectiva, i.e., análise de séries de log-retornos mensais observados, e a análise prospectiva, i.e., séries a ocorrer. No âmbito prospectivo, ex ante facto, o SRc (índice de Sharpe com variável de estado normal) e o SRd (índice de Sharpe com variável de estado lognormal), por estarem associados à função de utilidade quadrática, apresentam distorções como o ponto bliss e o agente econômico bomba de dinheiro. O mesmo ocorre no âmbito retrospectivo, ex post facto, com o GSR (potencial de desempenho de ativos de risco para indivíduos com função de utilidade HARA) quando o coeficiente de aversão ao risco é igual a um negativo, gama=-1. No entanto, o GSR pode ser associado a funções de utilidade diferentes da quadrática evitando seus efeitos indesejáveis. Sob a suposição de movimento browniano geométrico (MBG) e da utilidade HARA para o preço mensal ajustado de ações brasileiras e americanas e para pontos mensais de índices brasileiros e americanos, entre janeiro de 2000 e março de 2008, obtivemos os seguintes resultados: (1) o índice GSR para utilidade quadrática apresentou elevada correlação com o SRc; (2) a menor correlação de GSR com SRc ocorreu para utilidade logarítmica; (3) para a utilidade exponencial, o GSR apresenta elevado grau de correlação com o SRc. Os resultados mostraram que o GSR com utilidade exponencial é o índice que menos se aproxima do comportamento do GSR com utilidade quadrática. Sabendo-se das distorções da utilidade quadrática, a adoção do GSR com gama=1 parece mais adequado para a classificação de ativos de risco. / This master dissertation studies and compares the characteristics of Sharpe ratio and its variants, SRc and SRd, generalized Sharpe ratio (GSR) and investment potential (IP), both GSR and IP associated to any utility function. By the fact that GSR and IP are identical indexes, empiric tests were conducted between SRc and GSR. The indexes were evaluated theoretically under two different aspects: retrospective analysis, i.e., analyze the observed monthly log-returns, and prospective analysis, i.e., series to occur. Under prospective view, ex ante facto, SRc (Sharpe ratio with normal state variable) and SRd (Sharpe ratio with lognormal state variable), for being associated to the quadratic utility function, show the inherent problems to utility functions such as the bliss point and the pump money economic agent. The same happens in a retrospective view, ex post facto, with the GSR (performance potential with HARA utility function family) when the risk aversion coefficient equals minus one, gama=-1. Therefore, the GSR can be associated to different utility functions avoiding the undesirable effects. Under the GBM (geometric Brownian motion) condition and HARA utility function for the Brazilian and American adjusted monthly stock prices and indexes monthly points during January 2000 and March 2008, we reached the following: (1) results indicate that GSR for quadratic utility has high correlation level with SRc; (2) while the logarithmic utility showed lowest correlation level between GSR and SRc; (3) exponential utilities showed a high level of correlation between GSR and SRc. The results showed that GSR with exponential utility kept the biggest behavior difference for the GSR with quadratic utility. Based on the knowing problems of the quadratic utility, GSR with gama=1 seems to be a better index choice for risk assets classification.

Tesis por compendio / [ES] Los métodos multicriterio proporcionan un enfoque analítico y estructurado para la toma de decisiones en la gestión de la cadena de suministro, que permiten evaluaciones basadas en múltiples criterios, esenciales para gestionar socios comerciales sostenibles. El objetivo de esta tesis es contribuir a la gestión sostenible de la cadena de suministro desarrollando nuevos modelos y técnicas multicriterio para evaluar proveedores y clientes. Se han diseñado modelos que incorporan las preferencias empresariales para tomar decisiones colaborativas en la selección y clasificación transparente de alternativas basadas en criterios sostenibles. También se han desarrollado métodos para clasificar las alternativas en grupos ordenados y evaluar su calidad. Tanto los modelos como los métodos se han validado mediante casos empíricos y comparado con enfoques alternativos. La metodología se basa en una profunda revisión bibliográfica y en el conocimiento experto de profesionales en la cadena de suministro. Los modelos multicriterio propuestos emplean técnicas como el Analytic Hierarchy Process (AHP), la Multi-Attribute Utility Theory (MAUT) y PROMETHEE. También se han desarrollado tres algoritmos para la clasificación de alternativas (nominal y ordenada). En primer lugar, se ha propuesto un modelo multicriterio híbrido y se ha validado con datos reales para homologar y seleccionar proveedores de tecnología, así como para su priorización y clasificación. Este modelo integra métodos compensatorios (AHP, MAUT) y no compensatorios (PROMETHEE, FlowSort) en una jerarquía con criterios de sostenibilidad. La validación del modelo en un contexto real y su comparación con un modelo alternativo ha demostrado su capacidad para proporcionar información relevante y transparente en la toma de decisiones para la evaluación sostenible de proveedores de tecnología en el sector bancario. En segundo lugar, se ha diseñado un nuevo algoritmo, denominado Global Local Net Flow sorting (GLNF sorting), que clasifica alternativas en grupos ordenados a partir de los flujos netos generados en búsquedas globales y locales con PROMETHEE. Adicionalmente, se ha diseñado el algoritmo SILhouette for Sorting (SILS) para calcular un índice de calidad en las clasificaciones. Ambos algoritmos se han validado empíricamente en la segmentación de proveedores y sus resultados se han comparado con otros métodos publicados. Por una parte, GLNF sorting destaca al mejorar la discriminación entre proveedores cercanos a los perfiles limitantes de los grupos, aprovechando el nivel de similitud preferencial entre alternativas. Por otra, SILS mejora la calidad de las asignaciones y permite un análisis detallado que facilita la toma de decisiones. En tercer lugar, se ha propuesto un sistema de segmentación de clientes B2B basado en transacciones y colaboración, aplicando AHP y GLNF sorting. Validado con 8,157 clientes de una multinacional, se ha evaluado con SILS y estadística descriptiva. Comparado con K-means, el modelo genera clasificaciones más homogéneas y robustas. Esta herramienta permite a las empresas automatizar decisiones y llevar a cabo análisis detallados para mejorar las relaciones con los clientes, alineándose con sus estrategias de colaboración y enfoques de mercado. En cuarto lugar, las búsquedas globales y locales se han utilizado para proponer un algoritmo de clasificación nominal basado dos dimensiones, que proporciona una matriz estratégica muy útil para los gestores de cadena de suministro. Por último, se ha desarrollado el paquete de software PrometheeTools en R, que automatiza la aplicación de PROMETHEE, GLNF sorting y SILS para resolver problemas multicriterio de priorización y clasificación de alternativas. Este paquete se ha validado con éxito y destaca por su eficiencia en PROMETHEE con miles de alternativas. Está disponible en acceso abierto en el repositorio CRAN para su utilización por investigadores y profesionales interesados en toma de decisiones multicriterio. / [CA] Els mètodes multicriteri proporcionen un enfocament analític i estructurat per a la presa de decisions en la gestió de la cadena de subministrament, que permeten avaluacions basades en múltiples criteris, essencials per a gestionar socis comercials sostenibles. L'objectiu d'aquesta tesi és contribuir a la gestió sostenible de la cadena de subministrament desenvolupant nous models i tècniques multicriteri per a avaluar proveïdors i clients. S'han dissenyat models que incorporen les preferències empresarials per a prendre decisions col·laboratives en la selecció i classificació transparent d'alternatives basades en criteris sostenibles. També s'han desenvolupat mètodes per a classificar les alternatives en grups ordenats i avaluar-ne la qualitat. Tant els models com els mètodes s'han validat mitjançant casos empírics i comparat amb enfocaments alternatius. La metodologia es basa en una profunda revisió bibliogràfica i en el coneixement expert de professionals en la cadena de subministrament. Els models multicriteri proposats empren tècniques com ara el procés analític jeràrquic (AHP), la teoria d'utilitat multiatribut (MAUT) i PROMETHEE. També s'han desenvolupat tres algoritmes per a la classificació d'alternatives (nominal i ordenada). En primer lloc, s'ha proposat un model multicriteri híbrid i s'ha validat amb dades reals per a homologar i seleccionar proveïdors de tecnologia, així com per a la seua priorització i classificació. Aquest model integra mètodes compensatoris (AHP, MAUT) i no compensatoris (PROMETHEE, FlowSort) en una jerarquia amb criteris de sostenibilitat. La validació del model en un context real i la seua comparació amb un model alternatiu n'ha demostrat la capacitat per a proporcionar informació rellevant i transparent en la presa de decisions per a l'avaluació sostenible de proveïdors de tecnologia en el sector bancari. En segon lloc, s'ha dissenyat un nou algoritme, denominat Global Local Net Flow sorting (GLNF sorting), que classifica alternatives en grups ordenats a partir dels fluxos nets generats en cerques globals i locals amb PROMETHEE. Addicionalment, s'ha dissenyat l'algoritme SILhouette for Sorting (SILS) per a calcular un índex de qualitat en les classificacions. Ambdós algoritmes s'han validat empíricament en la segmentació de proveïdors i els seus resultats s'han comparat amb altres mètodes publicats. D'una banda, GLNF sorting destaca en millorar la discriminació entre proveïdors pròxims als perfils limitants dels grups, que aprofita el nivell de similitud preferencial entre alternatives. De l'altra, SILS millora la qualitat de les assignacions i permet una anàlisi detallada que facilita la presa de decisions. En tercer lloc, s'ha proposat un sistema de segmentació de clients B2B basat en transaccions i col·laboració, aplicant AHP i GLNF sorting. Validat amb 8,157 clients d'una multinacional, s'ha avaluat amb SILS i estadística descriptiva. Comparat amb K-means, el model genera classificacions més homogènies i robustes. Aquesta eina permet a les empreses automatitzar decisions i portar a cap anàlisis detallades per a millorar les relacions amb els clients, que s'alineen amb les seues estratègies de col·laboració i enfocaments de mercat. En quart lloc, les cerques globals i locals s'han utilitzat per a proposar un algoritme de classificació nominal basat en dues dimensions, que proporciona una matriu estratègica molt útil per als gestors de la cadena de subministrament. Finalment, s'ha desenvolupat el paquet de programari PrometheeTools en R, que automatitza l'aplicació de PROMETHEE, GLNF sorting i SILS per a resoldre problemes multicriteri de priorització i classificació d'alternatives. Aquest paquet s'ha validat amb èxit i destaca per la seua eficiència en PROMETHEE amb milers d'alternatives. Està disponible en accés obert en el repositori CRAN per a la utilització per investigadors i professionals interessats en la presa de decisions multicriteri. / [EN] Multicriteria methods provide an analytical and structured approach to decision making in supply chain management. These techniques allow multicriteria evaluations, which are essential for choosing and managing sustainable business partners. The aim of this thesis is to contribute to sustainable supply chain management by developing new multicriteria models and techniques to assess suppliers and customers. Models have been designed in order to incorporate business preferences to make collaborative decisions in the transparent selection and ranking of alternatives based on sustainable criteria. New methods have also been developed to classify alternatives into ordered groups and to assess their quality. Both models and methods have been validated using empirical cases and compared with alternative approaches. The methodology is based on an in-depth literature review, as well as the expertise of supply chain professionals. The proposed multicriteria models integrate techniques such as the Analytical Hierarchical Process (AHP), Multi-Attribute Utility Theory (MAUT) and the PROMETHEE method. Three new algorithms have also been developed for classifying alternatives into nominal and ordered groups (sorting problem). Firstly, a hybrid multicriteria model has been proposed and validated with real data for technology supplier qualifying, selection and ranking. This model integrates compensatory (AHP, MAUT) and non-compensatory (PROMETHEE, FlowSort) methods in a hierarchy with sustainability criteria to evaluate products, suppliers and manufacturers. Validation of the model in a real context and its comparison with an alternative model has demonstrated its ability to provide relevant and transparent information for decision making in the sustainable evaluation of technology suppliers in the banking sector. Secondly, a new algorithm has been designed, called Global Local Net Flow sorting (GLNF sorting), which classifies alternatives into ordered groups based on the net flows generated in global and local searches with PROMETHEE. In addition, the SILhouette for Sorting (SILS) algorithm has been designed to calculate a quality index in the classifications. Both algorithms have been empirically validated in supplier segmentation and their results compared with other published methods. On the one hand, the GLNF sorting algorithm excels in improving the discrimination between suppliers close to the limiting profiles by exploiting the level of preference similarity between alternatives. On the other, SILS improves the quality of alternative assignments to groups, allows for a detailed analysis of suppliers and facilitates decision making. Thirdly, a customer segmentation model based on transactions and collaboration has been proposed in the Business to Business context, applying AHP and GLNF sorting. Validated with 8,157 customers of a multinational company, it has been assessed by SILS and descriptive statistics. This model generates more homogeneous and robust groups than the K-means cluster method. This tool enables companies to automate decisions and perform detailed analysis to improve customer relationships, aligning with their collaboration strategies and market approaches. Fourthly, global and local searches have been used to propose an algorithm for nominal classification based on two dimensions, which provides a very useful strategic matrix for supply chain managers. Finally, the PrometheeTools software package has been developed in R, which automates the implementation of PROMETHEE, GLNF sorting and SILS to solve multicriteria problems of alternatives ranking and classification. This package has been successfully validated and stands out for the efficiency in PROMETHEE and especially when solving problems with thousands of alternatives. It is available by open access in the CRAN repository for use by researchers and practitioners interested in multicriteria decision making. / Barrera Jimenez, IF. (2024). Multicriteria Techniques for Sustainable Supply Chain Management [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/202879 / Compendio

A gap exists in the methods used in industry and available in academia that prevents customers and engineers from having a voice when considering engineering risk appetite in the dynamic shaping of early-phase conceptual design trade study outcomes. Current methods used in Collaborative Design Centers either collect risk information after a conceptual design has been created, treat risk as an afterthought during the trade study process, or do not consider risk at all during the creation of conceptual designs. This dissertation proposes a risk-informed decision making framework that offers a new way to account for risk and make decisions based upon risk information within conceptual complex system design trade studies. A meaningful integration of the consideration of risk in trade studies is achieved in this framework thus elevating risk to the same level as other important system-level design parameters. Trade-offs based upon risk appetites of individuals are explicitly allowed under the framework, enabled by an engineering-specific psychometric risk survey that provides aspirational information to use in utility functions. This dissertation provides a novel framework and supporting methodologies for risk-informed design decisions and trades to be made that are based upon engineering risk appetites in conceptual design trade studies. / Graduation date: 2012

Many have used the principles of statistics and Bayesian decision theory to model specific learning problems. It is less common to see models of the processes of learning in general. One exception is the model of the supervised learning process known as the "Extended Bayesian Formalism" or EBF. This model is descriptive, in that it can describe and compare learning algorithms. Thus the EBF is capable of modeling both effective and ineffective learning algorithms. We extend the EBF to model un-supervised learning, semi-supervised learning, supervised learning, and empirical function optimization. We also generalize the utility model of the EBF to deal with non-deterministic outcomes, and with utility functions other than 0-1 loss. Finally, we modify the EBF to create a "prescriptive" learning model, meaning that, instead of describing existing algorithms, our model defines how learning should optimally take place. We call the resulting model the Unified Bayesian Decision Theoretical Model, or the UBDTM. WE show that this model can serve as a cohesive theory and framework in which a broad range of questions can be analyzed and studied. Such a broadly applicable unified theoretical framework is one of the major missing ingredients of machine learning theory. Using the UBDTM, we concentrate on supervised learning and empirical function optimization. We then use the UBDTM to reanalyze many important theoretical issues in Machine Learning, including No-Free-Lunch, utility implications, and active learning. We also point forward to future directions for using the UBDTM to model learnability, sample complexity, and ensembles. We also provide practical applications of the UBDTM by using the model to train a Bayesian variation to the CMAC supervised learner in closed form, to perform a practical empirical function optimization task, and as part of the guiding principles behind an ongoing project to create an electronic and print corpus of tagged ancient Syriac texts using active learning.