Python Library for Consumer Decision Support System with Automatic Identification of Preferences

The development of information systems (IS) has increased in the e-commerce field. The need for continuous improvement of decision support systems implies the integration of multiple methodologies such as expert knowledge, data mining, big data, artificial intelligence, and multicriteria decision analysis (MCDA) methods. Artificial intelligence algorithms have proven their effectiveness as an engine for data-driven information systems. MCDA methods demonstrated usefulness in domains dealing with multiple dimensions. One of the most critical points of any MCDA procedure is criteria weighting using subjective or objective methods. However, both approaches have several limitations when there is a need to map the preferences of unavailable experts. EVO-SPOTIS library integrating a stochastic evolutionary algorithm with the MCDA method, introduced in this paper, attempts to address this problem. In this approach, the Differential Evolution (DE) algorithm is used to identify decision-makers’ preferences based on datasets evaluated by experts in the past. The Stable Preference Ordering Towards Ideal Solution (SPOTIS) method is used to compute the DE objective function’s values and perform the final evaluation of alternatives using the identified weights. Results confirm the high potential of the library for identification preferences and modeling customer behavior

Eco-design of chemical processes: an integrated approach coupling process modeling, life cycle assessment and multi-objective optimization

The objective of this work is to propose an integrated and generic framework for eco-design coupling traditional modelling and flowsheeting simulation tools (HYSYS, COCO, ProSimPlus and Ariane), Life Cycle Assessment, multi-objective optimization based on Genetic Algorithms and multiple criteria decision-making methods MCDM (Multiple Choice Decision Making, such as ELECTRE, PROMETHEE, M-TOPSIS) that generalizes, automates and optimizes the evaluation of the environmental criteria at earlier design stage. The approach consists of three main stages. The first two steps correspond respectively to process inventory analysis based on mass and energy balances and impact assessment phases of LCA methodology. Specific attention is paid to the main issues that can be encountered with database and impact assessment i.e. incomplete or missing information, or approximate information that does not match exactly the real situation that may introduce a bias in the environmental impact estimation. A process simulation tool dedicated to production utilities, Ariane, ProSim SA is used to fill environmental database gap, by the design of specific energy sub modules, so that the life cycle energy related emissions for any given process can be computed. The third stage of the methodology is based on the interaction of the previous steps with process simulation for environmental impact assessment and cost estimation through a computational framework. The use of multi-objective optimization methods generally leads to a set of efficient solutions, the so-called Pareto front. The next step consists in identifying the best ones through MCDM methods. The approach is applied to two processes operating in continuous mode. The capabilities of the methodology are highlighted through these case studies (benzene production by HDA process and biodiesel production from vegetable oils). A multi-level assessment for multi-objective optimization is implemented for both cases, the explored pathways depending on the analysis and antagonist behaviour of the criteria

Écoconception de procédés : approche systémique couplant modélisation globale, analyse du cycle de vie et optimisation multiobjectif

L’objectif de ce travail est de développer un cadre méthodologique et générique d’éco-conception de procédés chimiques couplant des outils de modélisation et de simulation traditionnels de procédés (HYSYS, COCO, ProSimPlus et Ariane), d’Analyse du Cycle de Vie (ACV), d’optimisation multiobjectif basée sur des Algorithmes Génétiques et enfin des outils d’aide à la décision multicritère (ELECTRE, PROMETHEE, M-TOPSIS). Il s’agit de généraliser, d’automatiser et d’optimiser l’évaluation des impacts environnementaux au stade préliminaire de la conception d’un procédé chimique. L’approche comprend trois étapes principales. Les deux premières correspondent d’une part aux phases d’analyse de l’inventaire par calcul des bilans de matière et d’énergie et d’autre part à l’évaluation environnementale par ACV. Le problème du manque d’information ou de l’imprécision dans les bases de données classiques en ACV pour la production d’énergie notamment sous forme de vapeur largement utilisée dans les procédés a reçu une attention particulière. Une solution proposée consiste à utiliser un simulateur de procédés de production d’utilités (Ariane, ProSim SA) pour contribuer à alimenter la base de données environnementale en tenant compte de variations sur les conditions opératoires ou sur les technologies utilisées. Des sous-modules « énergie » sont ainsi proposés pour calculer les émissions relatives aux impacts liés à l’utilisation de l’énergie dans les procédés. La troisième étape réalise l’interaction entre les deux premières phases et l’optimisation multi-objectif qui met en jeu des critères économiques et environnementaux. Elle conduit à des solutions de compromis le long du front de Pareto à partir desquelles les meilleures sont choisies à l’aide de méthodes d’aide à la décision. L’approche est appliquée à des procédés de production continus : production de benzène par hydrodéalkylation du toluène HDA et production de biodiesel à partir d’huiles végétales. Une stratégie à plusieurs niveaux est mise en oeuvre pour l'analyse de l'optimisation multi-objectif. Elle est utilisée dans les deux cas d'étude afin d'analyser les comportements antagonistes des critères. ABSTRACT : The objective of this work is to propose an integrated and generic framework for eco-design coupling traditional modelling and flowsheeting simulation tools (HYSYS, COCO, ProSimPlus and Ariane), Life Cycle Assessment, multi-objective optimization based on Genetic Algorithms and multiple criteria decision-making methods MCDM (Multiple Choice Decision Making, such as ELECTRE, PROMETHEE, M-TOPSIS) that generalizes, automates and optimizes the evaluation of the environmental criteria at earlier design stage. The approach consists of three main stages. The first two steps correspond respectively to process inventory analysis based on mass and energy balances and impact assessment phases of LCA methodology. Specific attention is paid to the main issues that can be encountered with database and impact assessment i.e. incomplete or missing information, or approximate information that does not match exactly the real situation that may introduce a bias in the environmental impact estimation. A process simulation tool dedicated to production utilities, Ariane, ProSim SA is used to fill environmental database gap, by the design of specific energy sub modules, so that the life cycle energy related emissions for any given process can be computed. The third stage of the methodology is based on the interaction of the previous steps with process simulation for environmental impact assessment and cost estimation through a computational framework. The use of multi-objective optimization methods generally leads to a set of efficient solutions, the so-called Pareto front. The next step consists in identifying the best ones through MCDM methods. The approach is applied to two processes operating in continuous mode. The capabilities of the methodology are highlighted through these case studies (benzene production by HDA process and biodiesel production from vegetable oils). A multi-level assessment for multi-objective optimization is implemented for both cases, the explored pathways depending on the analysis and antagonist behaviour of the criteria

A contribution to support decision making in energy/water sypply chain optimisation

The seeking of process sustainability forces enterprises to change their operations. Additionally, the industrial globalization implies a very dynamic market that, among other issues, promotes the enterprises competition. Therefore, the efficient control and use of their Key Performance Indicators, including profitability, cost reduction, demand satisfaction and environmental impact associated to the development of new products, is a significant challenge. All the above indicators can be efficiently controlled through the Supply Chain Management. Thus, companies work towards the optimization of their individual operations under competitive environments taking advantage of the flexibility provided by the virtually inexistent world market restrictions. This is achieved by the coordination of the resource flows, across all the entities and echelons belonging to the system network. Nevertheless, such coordination is significantly complicated if considering the presence of uncertainty and even more if seeking for a win-win outcome. The purpose of this thesis is extending the current decision making strategies to expedite these tasks in industrial processes. Such a contribution is based on the development of efficient mathematical models that allows coordinating large amount of information synchronizing the production and distribution tasks in terms of economic, environmental and social criteria. This thesis starts presents an overview of the requirements of sustainable production processes, describing and analyzing the current methods and tools used and identifying the most relevant open issues. All the above is always within the framework of Process System Engineering literature. The second part of this thesis is focused in stressing the current Multi-Objective solution strategies. During this part, first explores how the profitability of the Supply Chain can be enhanced by considering simultaneously multiple objectives under demand uncertainties. Particularly, solution frameworks have been proposed in which different multi-criteria decision making strategies have been combined with stochastic approaches. Furthermore, additional performance indicators (including financial and operational ones) have been included in the same solution framework to evaluate its capabilities. This framework was also applied to decentralized supply chains problems in order to explore its capabilities to produce solution that improves the performances of each one of the SC entities simultaneously. Consequently, a new generalized mathematical formulation which integrates many performance indicators in the production process within a supply chain is efficiently solved. Afterwards, the third part of the thesis extends the proposed solution framework to address the uncertainty management. Particularly, the consideration of different types and sources of uncertainty (e.g. external and internal ones) where considered, through the implementation of preventive approaches. This part also explores the use of solution strategies that efficiently selects the number of scenarios that represent the uncertainty conditions. Finally, the importance and effect of each uncertainty source over the process performance is detailed analyzed through the use of surrogate models that promote the sensitivity analysis of those uncertainties. The third part of this thesis is focused on the integration of the above multi-objective and uncertainty approaches for the optimization of a sustainable Supply Chain. Besides the integration of different solution approaches, this part also considers the integration of hierarchical decision levels, by the exploitation of mathematical models that assess the consequences of considering simultaneously design and planning decisions under centralized and decentralized Supply Chains. Finally, the last part of this thesis provides the final conclusions and further work to be developed.La globalización industrial genera un ambiente dinámico en los mercados que, entre otras cosas, promueve la competencia entre corporaciones. Por lo tanto, el uso eficiente de las los indicadores de rendimiento, incluyendo rentabilidad, satisfacción de la demanda y en general el impacto ambiental, representa un area de oportunidad importante. El control de estos indicadores tiene un efecto positivo si se combinan con la gestión de cadena de suministro. Por lo tanto, las compañías buscan definir sus operaciones para permanecer activas dentro de un ambiente competitivo, tomando en cuenta las restricciones en el mercado mundial. Lo anterior puede ser logrado mediante la coordinación de los flujos de recursos a través de todas las entidades y escalones pertenecientes a la red del sistema. Sin embargo, dicha coordinación se complica significativamente si se quiere considerar la presencia de incertidumbre, y aún más, si se busca exclusivamente un ganar-ganar. El propósito de esta tesis es extender el alcance de las estrategias de toma de decisiones con el fin de facilitar estas tareas dentro de procesos industriales. Estas contribuciones se basan en el desarrollo de modelos matemáticos eficientes que permitan coordinar grandes cantidades de información sincronizando las tareas de producción y distribución en términos económicos, ambientales y sociales. Esta tesis inicia presentando una visión global de los requerimientos de un proceso de producción sostenible, describiendo y analizando los métodos y herramientas actuales así como identificando las áreas de oportunidad más relevantes dentro del marco de ingeniería de procesos La segunda parte se enfoca en enfatizar las capacidades de las estrategias de solución multi-objetivo, durante la cual, se explora el mejoramiento de la rentabilidad de la cadena de suministro considerando múltiples objetivos bajo incertidumbres en la demanda. Particularmente, diferentes marcos de solución han sido propuestos en los que varias estrategias de toma de decisión multi-criterio han sido combinadas con aproximaciones estocásticas. Por otra parte, indicadores de rendimiento (incluyendo financiero y operacional) han sido incluidos en el mismo marco de solución para evaluar sus capacidades. Este marco fue aplicado también a problemas de cadenas de suministro descentralizados con el fin de explorar sus capacidades de producir soluciones que mejoran simultáneamente el rendimiento para cada uno de las entidades dentro de la cadena de suministro. Consecuentemente, una nueva formulación que integra varios indicadores de rendimiento en los procesos de producción fue propuesta y validada. La tercera parte de la tesis extiende el marco de solución propuesto para abordar el manejo de incertidumbres. Particularmente, la consideración de diferentes tipos y fuentes de incertidumbre (p.ej. externos e internos) fueron considerados, mediante la implementación de aproximaciones preventivas. Esta parte también explora el uso de estrategias de solución que elige eficientemente el número de escenarios necesario que representan las condiciones inciertas. Finalmente, la importancia y efecto de cada una de las fuentes de incertidumbre sobre el rendimiento del proceso es analizado en detalle mediante el uso de meta modelos que promueven el análisis de sensibilidad de dichas incertidumbres. La tercera parte de esta tesis se enfoca en la integración de las metodologías de multi-objetivo e incertidumbre anteriormente expuestas para la optimización de cadenas de suministro sostenibles. Además de la integración de diferentes métodos. Esta parte también considera la integración de diferentes niveles jerárquicos de decisión, mediante el aprovechamiento de modelos matemáticos que evalúan lasconsecuencias de considerar simultáneamente las decisiones de diseño y planeación de una cadena de suministro centralizada y descentralizada. La parte final de la tesis detalla las conclusiones y el trabajo a futuro necesario sobre esta línea de investigaciónPostprint (published version

Capturing Risk in Capital Budgeting

NPS NRP Technical ReportThis proposed research has the goal of proposing novel, reusable, extensible, adaptable, and comprehensive advanced analytical process and Integrated Risk Management to help the (DOD) with risk-based capital budgeting, Monte Carlo risk-simulation, predictive analytics, and stochastic optimization of acquisitions and programs portfolios with multiple competing stakeholders while subject to budgetary, risk, schedule, and strategic constraints. The research covers topics of traditional capital budgeting methodologies used in industry, including the market, cost, and income approaches, and explains how some of these traditional methods can be applied in the DOD by using DOD-centric non-economic, logistic, readiness, capabilities, and requirements variables. Stochastic portfolio optimization with dynamic simulations and investment efficient frontiers will be run for the purposes of selecting the best combination of programs and capabilities is also addressed, as are other alternative methods such as average ranking, risk metrics, lexicographic methods, PROMETHEE, ELECTRE, and others. The results include actionable intelligence developed from an analytically robust case study that senior leadership at the DOD may utilize to make optimal decisions. The main deliverables will be a detailed written research report and presentation brief on the approach of capturing risk and uncertainty in capital budgeting analysis. The report will detail the proposed methodology and applications, as well as a summary case study and examples of how the methodology can be applied.N8 - Integration of Capabilities & ResourcesThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Multidimensional approaches to performance evaluation of competing forecasting models

The purpose of my research is to contribute to the field of forecasting from a methodological perspective as well as to the field of crude oil as an application area to test the performance of my methodological contributions and assess their merits. In sum, two main methodological contributions are presented. The first contribution consists of proposing a mathematical programming based approach, commonly referred to as Data Envelopment Analysis (DEA), as a multidimensional framework for relative performance evaluation of competing forecasting models or methods. As opposed to other performance measurement and evaluation frameworks, DEA allows one to identify the weaknesses of each model, as compared to the best one(s), and suggests ways to improve their overall performance. DEA is a generic framework and as such its implementation for a specific relative performance evaluation exercise requires a number of decisions to be made such as the choice of the units to be assessed, the choice of the relevant inputs and outputs to be used, and the choice of the appropriate models. In order to present and discuss how one might adapt this framework to measure and evaluate the relative performance of competing forecasting models, we first survey and classify the literature on performance criteria and their measures – including statistical tests – commonly used in evaluating and selecting forecasting models or methods. In sum, our classification will serve as a basis for the operationalisation of DEA. Finally, we test DEA performance in evaluating and selecting models to forecast crude oil prices. The second contribution consists of proposing a Multi-Criteria Decision Analysis (MCDA) based approach as a multidimensional framework for relative performance evaluation of the competing forecasting models or methods. In order to present and discuss how one might adapt such framework, we first revisit MCDA methodology, propose a revised methodological framework that consists of a sequential decision making process with feedback adjustment mechanisms, and provide guidelines as to how to operationalise it. Finally, we adapt such a methodological framework to address the problem of performance evaluation of competing forecasting models. For illustration purposes, we have chosen the forecasting of crude oil prices as an application area

Multi-criteria decision methods to support the maintenance management of complex systems

[ES] Esta tesis doctoral propone el uso de métodos de toma de decisiones multi-criterio (MCDM, por sus iniciales en inglés) como herramienta estratégica para apoyar la gestión del mantenimiento de sistemas complejos. El desarrollo de esta tesis doctoral se enmarca dentro de un acuerdo de cotutela entre la Università degli Studi di Palermo (UNIPA) y la Universitat Politècnica de València (UPV), dentro de sus respectivos programas de doctorado en 'Ingeniería de Innovación Tecnológica' y 'Matemáticas'. Estos programas están estrechamente vinculados a través del tópico MCDM, ya que proporciona herramientas cruciales para gestionar el mantenimiento de sistemas complejos reales utilizando análisis matemáticos serios. El propósito de esta sinergia es tener en cuenta de forma sólida la incertidumbre al atribuir evaluaciones subjetivas, recopilar y sintetizar juicios atribuidos por varios responsables de la toma de decisiones, y tratar con conjuntos grandes de esos elementos. El tema principal del presente trabajo de doctorado es el gestionamiento de las actividades de mantenimiento para aumentar los niveles de innovación tecnológica y el rendimiento de los sistemas complejos. Cualquier sistema puede ser considerado objeto de estudio, incluidos los sistemas de producción y los de prestación de servicios, entre otros, mediante la evaluación de sus contextos reales. Esta tesis doctoral propone afrontar la gestión del mantenimiento a través del desarrollo de tres líneas principales de investigación estrechamente vinculadas. ¿ La primera es el núcleo, e ilustra la mayoría de los aspectos metodológicos de la tesis. Se refiere al uso de métodos MCDM para apoyar decisiones estratégicas de mantenimiento, y para hacer frente a la incertidumbre que afecta a los datos/evaluaciones, incluso cuando están involucrados varios responsables (expertos en mantenimiento) en la toma de decisiones. ¿ La segunda línea desarrolla análisis de fiabilidad para sistemas complejos reales (también en términos de fiabilidad humana) sobre cuya base se debe implementar cualquier actividad de mantenimiento. Estos análisis consideran la configuración de fiabilidad de los componentes del sistema en estudio y las características específicas del entorno operativo. ¿ La tercera línea de investigación aborda aspectos metodológicos importantes de la gestión de mantenimiento y enfatiza la necesidad de monitorizar el funcionamiento de las actividades de mantenimiento y de evaluar su efectividad utilizando indicadores adecuados. Se ha elaborado una amplia gama de casos de estudio del mundo real para evaluar la eficacia de los métodos MCDM en el mantenimiento y así probar la utilidad del enfoque propuesto.[CA] Aquesta tesi doctoral proposa l'ús de mètodes de presa de decisions multi-criteri (MCDM, per les seves inicials en anglès) com a eina estratègica per donar suport a la gestió del manteniment de sistemes complexos. El desenvolupament d'aquesta tesi doctoral s'emmarca dins d'un acord de cotutela entre la Università degli Studi di Palermo (UNIPA) i la Universitat Politècnica de València (UPV), dins dels seus respectius programes de doctorat en 'Enginyeria d'Innovació Tecnològica' i ' Matemàtiques '. Aquests programes estan estretament vinculats a través del tòpic MCDM, ja que proporciona eines crucials per gestionar el manteniment de sistemes complexos reals utilitzant anàlisis matemàtics profunds. El propòsit d'aquesta sinergia és tenir en compte de forma sòlida la incertesa en atribuir avaluacions subjectius, recopilar i sintetitzar judicis atribuïts per diversos responsables de la presa de decisions, i tractar amb conjunts grans d'aquests elements en els problemes plantejats. El tema principal del present treball de doctorat es la gestió de les activitats de manteniment per augmentar els nivells d'innovació tecnològica i el rendiment dels sistemes complexos. Qualsevol sistema pot ser considerat objecte d'estudi, inclosos els sistemes de producció i els de prestació de serveis, entre d'altres, mitjançant l'avaluació dels seus contextos reals. Aquesta tesi doctoral proposa afrontar la gestió del manteniment mitjançant el desenvolupament de tres línies principals d'investigació estretament vinculades. ¿ La primera és el nucli, i il·lustra la majoria dels aspectes metodològics de la tesi. Es refereix a l'ús de mètodes MCDM per donar suport a decisions estratègiques de manteniment, i per fer front a la incertesa que afecta les dades/avaluacions, fins i tot quan estan involucrats diversos responsables (experts en manteniment) en la presa de decisions. ¿ La segona línia desenvolupa anàlisis de fiabilitat per a sistemes complexos reals (també en termes de fiabilitat humana) sobre la qual base s'ha d'implementar qualsevol activitat de manteniment. Aquestes anàlisis consideren la configuració de fiabilitat dels components del sistema en estudi i les característiques específiques de l'entorn operatiu. ¿ La tercera línia d'investigació aborda aspectes metodològics importants de la gestió de manteniment i emfatitza la necessitat de monitoritzar el funcionament de les activitats de manteniment i d'avaluar la seva efectivitat utilitzant indicadors adequats. S'ha elaborat una àmplia gamma de casos d'estudi del món real per avaluar l'eficàcia dels mètodes MCDM en el manteniment i així provar la utilitat de l'enfocament proposat.[EN] This doctoral thesis proposes using multi-criteria decision making (MCDM) methods as a strategic tool to support maintenance management of complex systems. The development of this doctoral thesis is framed within a cotutelle (co-tutoring) agreement between the Università degli Studi di Palermo (UNIPA) and the Universitat Politècnica de València (UPV), within their respective programmes of doctorates in 'Technological Innovation Engineering' and 'Mathematics'. Regarding this thesis, these programmes are closely linked through the topic of MCDM, providing crucial tools to manage maintenance of real complex systems by applying in-depth mathematical analyses. The purpose of this connection is to robustly take into account uncertainty in attributing subjective evaluations, collecting and synthetizing judgments attributed by various decision makers, and dealing with large sets of elements characterising the faced issue. The main topic of the present doctoral work is the management of maintenance activities to increase the levels of technological innovation and performance of the analysed complex systems. All kinds of systems can be considered as objects of study, including production systems and service delivery systems, among others, by evaluating their real contexts. Thus, this doctoral thesis proposes facing maintenance management through the development of three tightly linked main research lines. ¿ The first is the core and illustrates most of the methodological aspects of the thesis. It refers to the use of MCDM methods for supporting strategic maintenance decisions, and dealing with uncertainty affecting data/evaluations even when several decision makers are involved (experts in maintenance). ¿ The second line develops reliability analyses for real complex systems (also in terms of human reliability analysis) on the basis of which any maintenance activity must be implemented. These analyses are approached by considering the reliability configuration of both the components belonging to the system under study and the specific features of the operational environment. ¿ The third research line focuses on important methodological aspects to support maintenance management, and emphasises the need to monitor the performance of maintenance activities and evaluate their effectiveness using suitable indicators. A wide range of real real-world case studies has been faced to evaluate the effectiveness of MCDM methods in maintenance and then prove the usefulness of the proposed approach.Carpitella, S. (2019). Multi-criteria decision methods to support the maintenance management of complex systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/11911