Multi-objective Optimization of Industrial Ammonia Synthesis

The thesis describes modelling and optimization work of an industrial ammonia synthesis. Author developed first-principle mathematical model of the commercial converter based on gas-solid reaction and heat transfer within the system. The model is validated with industrial data and showed satisfactory accuracy. Further, optimization study is performed in multi-objective manner to intensify ammonia production and decrease heat duty of the process. Result have revealed a potential to improve current operating condition int terms of both objectives

Proposta de uma ferramenta para a resolução de problemas de trade-off na gestão de projetos

Atualmente a gestão de projetos implica a tomada de decisões face a problemas cada vez mais complexos, com mais variáveis e com mais objetivos a otimizar e/ou a cumprir que, por sua vez, são sistematicamente conflituosos entre si. Neste âmbito, surge a proposta de um framework composto por um método de otimização multiobjetivo e métodos multicritério de apoio à decisão que, numa primeira instância, permitem atribuir pesos aos critérios e, seguidamente, permitem ordenar as soluções, de forma a suportar a tomada de decisão por parte do gestor do projeto. O Arrefecimento Simulado Multiobjetivo foi o método de otimização multiobjetivo utilizado, pela sua simplicidade e versatilidade, tendo-se estudado o impacto da incorporação de duas estratégias de pesquisa (regresso ao arquivo e grelha exploratória), sendo que ambas contribuíram para a melhoria do desempenho do algoritmo e, consequentemente, para determinar as soluções de compromisso. A Entropia de Shannon e o AHP foram os métodos utilizados para a atribuição de pesos aos critérios, sendo que a primeira metodologia deverá ser aplicada por decisores mais inexperientes (uma vez que é objetiva e se baseia na informação existente) e a segunda permite uma ponderação mais subjetiva e flexível dos pesos dos critérios, com base na experiência do gestor de projeto. A atribuição dos pesos permite a utilização de métodos de ordenamento de soluções, como o TOPSIS e o VIKOR, que tendo o mesmo objetivo originam rankings diferentes. Com o objetivo de tornar o ranking das soluções mais robusto, sugere-se um método híbrido que conjuga os scores das duas metodologias, destacando as soluções que são melhores em ambos os métodos. A metodologia proposta permite resolver problemas de otimização multiobjetivo em problemas de trade-off, suportando as decisões do gestor de projeto. Contudo, as ferramentas utilizadas encontram aplicações em diversas áreas.Nowadays, project management implies the decision-making regarding problems with increased complexity, with more variables and objectives to optimize and/or to fulfil, which are conflicting with each other. In this context, it is proposed a framework composed by a multiobjective optimization method and multicriteria decision-making methods, which at first, allow for the weight attribution and, secondly, to rank the solutions allowing the project manager to support its decision. Multiobjective Simulated Annealing is the multiobjective optimization method chosen, due to its simplicity and versatility, allowing the study of the incorporation of two search strategies (return to archive and exploratory grid), which improved algorithm performance, increasing the number of non-dominated solutions found. Shannon’s entropy and AHP were the chosen methods for weighting criteria. Junior project managers should choose the first method (since it is objective and based in the existing information) and the latter is more subjective and flexible, relying on the decision’s experience. Weighing allows the ranking of the compromise solutions, like TOPSIS and VIKOR, which, although they the same objective, produce different rankings. In this context, an hybrid method combining both method were proposed to deliver a more robust ranking method, highlighting the best solutions from both methods. The methodology proposed allows the resolution of multiobjective optimization regarding trade-off problems, supporting the project manager’s decisions. However, the methods used might be used in many different fields

Optimización multiobjetivo de la distribución en planta de procesos industriales. Estudio de objetivos

En el proceso de diseño e las construcciones industriales, es de vital importancia conocer cual es la ubicación óptima de las diferentes áras de trabajo que conforman un proceso de fabricación, así como de las instalaciones y servicios auxiliares. El problema de distribución en planta (Facilities Layout Problem, FLP) integra a todas las actividades industriales y se ha convertido desde los años 60 en uno de los problemas clásicos de optimización combinatoria, en el que trabajan multiutd de investigadores a nivel internacional. Hasta los años 90, el enfoque que se realizaba del problema era básicamente un enfoque monobjetivo, en el que se primaba fundamentalmente la minimización del coste de transporte de material o personas entre las diferentes áreas productivas o de servicios. Para ello se han venido empleando diferentes técnicas de optimización heurística, que persiguen minimizar el tiempo de cálculo y facilitar la búsqueda de mínimos, aunque sean locales, pues el espacio de soluciones es tan grande, que es difícil garantizar la existencia de un mínimo global del problema. No obstante, el criterio de coste no es el único que se debe considerar en este tipo de planteamientos, pues existen otra serie de indicadores que son de vital importancia, para garantizar que la solución propuesta tiene un nivel de desarrollo tecnológico con la aparición de equipos y programas informáticos más desarrollados, han prosperado las aproximaciones multiobjetivos al problema de distribución en planta. Entre los objetivos principales del presente trabajo se encuentran; la realización de un estado del arte de los indicadores que se han empleado en la bibliografía para la resolución en planta, obteniendo un conjunto de indicadores independientes y suficientes que puedan ser empleados en la obtención de distribuciones en planta óptimas. Se investigará si es necesario definir algún nuevo indicador que cubra los objetivos fundamentales de la distribución en planta establecidos por distintos autores. Una vez seleccionados los indicadores se propone una técnica de optimización multiobjetivo basada en un algoritmo de recocido simulado (Simulated Annealing). Finalmente se presentan los resultados de los experimentos realizados, empleando la técnica de optimización multiobjetivo propuesta, sobre un problema ampliamente utilizado en la bibliografía, el propuesto por Armour y Buffa de 20 actividades. Se obtienen las fronteras de Pareto para diferentes bicriterios, introduciendo puntos que completan las existentes hasta la actualidad, estudiando la posibilidad de extender la optimización a 3 indicadores.Montalva Subirats, JM. (2011). Optimización multiobjetivo de la distribución en planta de procesos industriales. Estudio de objetivos [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/11147Palanci

Réagir et s’adapter à son environnement: Concevoir des méthodes autonomes pour l’optimisation combinatoire à plusieurs objectifs

Large-scale optimisation problems are usually hard to solve optimally. Approximation algorithms such as metaheuristics, able to quickly find sub-optimal solutions, are often preferred. This thesis focuses on multi-objective local search (MOLS) algorithms, metaheuristics able to deal with the simultaneous optimisation of multiple criteria. As many algorithms, metaheuristics expose many parameters that significantly impact their performance. These parameters can be either predicted and set before the execution of the algorithm, or dynamically modified during the execution itself.While in the last decade many advances have been made on the automatic design of algorithms, the great majority of them only deal with single-objective algorithms and the optimisation of a single performance indicator such as the algorithm running time or the final solution quality. In this thesis, we investigate the relations between automatic algorithm design and multi-objective optimisation, with an application on MOLS algorithms.We first review possible MOLS strategies ans parameters and present a general, highly configurable, MOLS framework. We also propose MO-ParamILS, an automatic configurator specifically designed to deal with multiple performance indicators. Then, we conduct several studies on the automatic offline design of MOLS algorithms on multiple combinatorial bi-objective problems. Finally, we discuss two online extensions of classical algorithm configuration: first the integration of parameter control mechanisms, to benefit from having multiple configuration predictions; then the use of configuration schedules, to sequentially use multiple configurations.Les problèmes d’optimisation à grande échelle sont généralement difficiles à résoudre de façon optimale. Des algorithmes d’approximation tels que les métaheuristiques, capables de trouver rapidement des solutions sous-optimales, sont souvent préférés. Cette thèse porte sur les algorithmes de recherche locale multi-objectif (MOLS), des métaheuristiques capables de traiter l’optimisation simultanée de plusieurs critères. Comme de nombreux algorithmes, les MOLS exposent de nombreux paramètres qui ont un impact important sur leurs performances. Ces paramètres peuvent être soit prédits et définis avant l’exécution de l’algorithme, soit ensuite modifiés dynamiquement.Alors que de nombreux progrès ont récemment été réalisés pour la conception automatique d’algorithmes, la grande majorité d’entre eux ne traitent que d’algorithmes mono-objectif et l’optimisation d’un unique indicateur de performance. Dans cette thèse, nous étudions les relations entre la conception automatique d’algorithmes et l’optimisation multi-objective.Nous passons d’abord en revue les stratégies MOLS possibles et présentons un framework MOLS général et hautement configurable. Nous proposons également MO-ParamILS, un configurateur automatique spécialement conçu pour gérer plusieurs indicateurs de performance. Nous menons ensuite plusieurs études sur la conception automatique de MOLS sur de multiples problèmes combinatoires bi-objectifs. Enfin, nous discutons deux extensions de la configuration d’algorithme classique : d’abord l’intégration des mécanismes de contrôle de paramètres, pour bénéficier de multiples prédictions de configuration; puis l’utilisation séquentielle de plusieurs configurations

Decision-maker Trade-offs In Multiple Response Surface Optimization

The focus of this dissertation is on improving decision-maker trade-offs and the development of a new constrained methodology for multiple response surface optimization. There are three key components of the research: development of the necessary conditions and assumptions associated with constrained multiple response surface optimization methodologies; development of a new constrained multiple response surface methodology; and demonstration of the new method. The necessary conditions for and assumptions associated with constrained multiple response surface optimization methods were identified and found to be less restrictive than requirements previously described in the literature. The conditions and assumptions required for a constrained method to find the most preferred non-dominated solution are to generate non-dominated solutions and to generate solutions consistent with decision-maker preferences among the response objectives. Additionally, if a Lagrangian constrained method is used, the preservation of convexity is required in order to be able to generate all non-dominated solutions. The conditions required for constrained methods are significantly fewer than those required for combined methods. Most of the existing constrained methodologies do not incorporate any provision for a decision-maker to explicitly determine the relative importance of the multiple objectives. Research into the larger area of multi-criteria decision-making identified the interactive surrogate worth trade-off algorithm as a potential methodology that would provide that capability in multiple response surface optimization problems. The ISWT algorithm uses an ε-constraint formulation to guarantee a non-dominated solution, and then interacts with the decision-maker after each iteration to determine the preference of the decision-maker in trading-off the value of the primary response for an increase in value of a secondary response. The current research modified the ISWT algorithm to develop a new constrained multiple response surface methodology that explicitly accounts for decision-maker preferences. The new Modified ISWT (MISWT) method maintains the essence of the original method while taking advantage of the specific properties of multiple response surface problems to simplify the application of the method. The MISWT is an accessible computer-based implementation of the ISWT. Five test problems from the multiple response surface optimization literature were used to demonstrate the new methodology. It was shown that this methodology can handle a variety of types and numbers of responses and independent variables. Furthermore, it was demonstrated that the methodology can be successful using a priori information from the decision-maker about bounds or targets or can use the extreme values obtained from the region of operability. In all cases, the methodology explicitly considered decision-maker preferences and provided non-dominated solutions. The contribution of this method is the removal of implicit assumptions and includes the decision-maker in explicit trade-offs among multiple objectives or responses