999 resultados para Pareto process


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This paper proposes different estimators for the parameters of SemiPareto and Pareto autoregressive minification processes The asymptotic properties of the estimators are established by showing that the SemiPareto process is α-mixing. Asymptotic variances of different moment and maximum likelihood estimators are compared.

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Le peculiarità del contesto competitivo attuale richiedono alle aziende di muoversi con la massima velocità per rispondere il più rapidamente possibile al soddisfacimento delle richieste dei clienti. La ricerca di massima flessibilità non può prescindere dall’esigenza di mantenere alti livelli di efficienza produttiva e di tendere ad un continuo miglioramento dei flussi interni. L’elaborato ripercorre i passaggi fondamentali di un progetto di miglioramento delle performance di un impianto svolto nel primo semestre 2016 presso Philip Morris Manufacturing & Technology Bologna S.p.A. La metodologia utilizzata riprende strumenti, modelli e metodi dai principi alla base del Focus Improvement, primo pilastro del tempio della Total Productive Maintenance. Attraverso l’applicazione sistematica di tecniche tipiche del problem solving (ciclo di Deming) e di approcci analitici per la determinazione delle cause di guasto (curva di Pareto, Diagramma di Ishikawa), è stato possibile identificare i principali tipi di perdite (tempo, performance, difetti qualitativi) di una macchina industriale e mettere in atto gli interventi migliorativi necessari. L’analisi si conclude con la valutazione dei futuri sviluppi dello scenario, proponendo diverse alternative a seconda dell’abilità dell’organizzazione di sostenere i risultati raggiunti o addirittura di superarli.

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Multi-objective optimization algorithms aim at finding Pareto-optimal solutions. Recovering Pareto fronts or Pareto sets from a limited number of function evaluations are challenging problems. A popular approach in the case of expensive-to-evaluate functions is to appeal to metamodels. Kriging has been shown efficient as a base for sequential multi-objective optimization, notably through infill sampling criteria balancing exploitation and exploration such as the Expected Hypervolume Improvement. Here we consider Kriging metamodels not only for selecting new points, but as a tool for estimating the whole Pareto front and quantifying how much uncertainty remains on it at any stage of Kriging-based multi-objective optimization algorithms. Our approach relies on the Gaussian process interpretation of Kriging, and bases upon conditional simulations. Using concepts from random set theory, we propose to adapt the Vorob’ev expectation and deviation to capture the variability of the set of non-dominated points. Numerical experiments illustrate the potential of the proposed workflow, and it is shown on examples how Gaussian process simulations and the estimated Vorob’ev deviation can be used to monitor the ability of Kriging-based multi-objective optimization algorithms to accurately learn the Pareto front.

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Over the past ten years, a variety of microRNA target prediction methods has been developed, and many of the methods are constantly improved and adapted to recent insights into miRNA-mRNA interactions. In a typical scenario, different methods return different rankings of putative targets, even if the ranking is reduced to selected mRNAs that are related to a specific disease or cell type. For the experimental validation it is then difficult to decide in which order to process the predicted miRNA-mRNA bindings, since each validation is a laborious task and therefore only a limited number of mRNAs can be analysed. We propose a new ranking scheme that combines ranked predictions from several methods and - unlike standard thresholding methods - utilises the concept of Pareto fronts as defined in multi-objective optimisation. In the present study, we attempt a proof of concept by applying the new ranking scheme to hsa-miR-21, hsa-miR-125b, and hsa-miR-373 and prediction scores supplied by PITA and RNAhybrid. The scores are interpreted as a two-objective optimisation problem, and the elements of the Pareto front are ranked by the STarMir score with a subsequent re-calculation of the Pareto front after removal of the top-ranked mRNA from the basic set of prediction scores. The method is evaluated on validated targets of the three miRNA, and the ranking is compared to scores from DIANA-microT and TargetScan. We observed that the new ranking method performs well and consistent, and the first validated targets are elements of Pareto fronts at a relatively early stage of the recurrent procedure. which encourages further research towards a higher-dimensional analysis of Pareto fronts. (C) 2010 Elsevier Ltd. All rights reserved.

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A bivariate semi-Pareto distribution is introduced and characterized using geometric minimization. Autoregressive minification models for bivariate random vectors with bivariate semi-Pareto and bivariate Pareto distributions are also discussed. Multivariate generalizations of the distributions and the processes are briefly indicated.

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Los sistemas de recomendación son un tipo de solución al problema de sobrecarga de información que sufren los usuarios de los sitios web en los que se pueden votar ciertos artículos. El sistema de recomendación de filtrado colaborativo es considerado como el método con más éxito debido a que sus recomendaciones se hacen basándose en los votos de usuarios similares a un usuario activo. Sin embargo, el método de filtrado de colaboración tradicional selecciona usuarios insuficientemente representativos como vecinos de cada usuario activo. Esto significa que las recomendaciones hechas a posteriori no son lo suficientemente precisas. El método propuesto en esta tesis realiza un pre-filtrado del proceso, mediante el uso de dominancia de Pareto, que elimina los usuarios menos representativos del proceso de selección k-vecino y mantiene los más prometedores. Los resultados de los experimentos realizados en MovieLens y Netflix muestran una mejora significativa en todas las medidas de calidad estudiadas en la aplicación del método propuesto. ABSTRACTRecommender systems are a type of solution to the information overload problem suffered by users of websites on which they can rate certain items. The Collaborative Filtering Recommender System is considered to be the most successful approach as it make its recommendations based on votes of users similar to an active user. Nevertheless, the traditional collaborative filtering method selects insufficiently representative users as neighbors of each active user. This means that the recommendations made a posteriori are not precise enough. The method proposed in this thesis performs a pre-filtering process, by using Pareto dominance, which eliminates the less representative users from the k-neighbor selection process and keeps the most promising ones. The results from the experiments performed on Movielens and Netflix show a significant improvement in all the quality measures studied on applying the proposed method.

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Presentation submitted to PSE Seminar, Chemical Engineering Department, Center for Advanced Process Design-making (CAPD), Carnegie Mellon University, Pittsburgh (USA), October 2012.

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This multidisciplinary study concerns the optimal design of processes with a view to both maximizing profit and minimizing environmental impacts. This can be achieved by a combination of traditional chemical process design methods, measurements of environmental impacts and advanced mathematical optimization techniques. More to the point, this paper presents a hybrid simulation-multiobjective optimization approach that at once optimizes the production cost and minimizes the associated environmental impacts of isobutane alkylation. This approach has also made it possible to obtain the flowsheet configurations and process variables that are needed to manufacture isooctane in a way that satisfies the above-stated double aim. The problem is formulated as a Generalized Disjunctive Programming problem and solved using state-of-the-art logic-based algorithms. It is shown, starting from existing alternatives for the process, that it is possible to systematically generate a superstructure that includes alternatives not previously considered. The optimal solution, in the form a Pareto curve, includes different structural alternatives from which the most suitable design can be selected. To evaluate the environmental impact, Life Cycle Assessment based on two different indicators is employed: Ecoindicator 99 and Global Warming Potential.

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Multiobjective Generalized Disjunctive Programming (MO-GDP) optimization has been used for the synthesis of an important industrial process, isobutane alkylation. The two objective functions to be simultaneously optimized are the environmental impact, determined by means of LCA (Life Cycle Assessment), and the economic potential of the process. The main reason for including the minimization of the environmental impact in the optimization process is the widespread environmental concern by the general public. For the resolution of the problem we employed a hybrid simulation- optimization methodology, i.e., the superstructure of the process was developed directly in a chemical process simulator connected to a state of the art optimizer. The model was formulated as a GDP and solved using a logic algorithm that avoids the reformulation as MINLP -Mixed Integer Non Linear Programming-. Our research gave us Pareto curves compounded by three different configurations where the LCA has been assessed by two different parameters: global warming potential and ecoindicator-99.

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Plasma sprayed aluminum oxide ceramic coating is widely used due to its outstanding wear, corrosion, and thermal shock resistance. But porosity is the integral feature in the plasma sprayed coating which exponentially degrades its properties. In this study, process maps were developed to obtain Al2O3-CNT composite coatings with the highest density (i.e. lowest porosity) and improved mechanical and wear properties. Process map is defined as a set of relationships that correlates large number of plasma processing parameters to the coating properties. Carbon nanotubes (CNTs) were added as reinforcement to Al2O 3 coating to improve the fracture toughness and wear resistance. Two novel powder processing approaches viz spray drying and chemical vapor growth were adopted to disperse CNTs in Al2O3 powder. The degree of CNT dispersion via chemical vapor deposition (CVD) was superior to spray drying but CVD could not synthesize powder in large amount. Hence optimization of plasma processing parameters and process map development was limited to spray dried Al2O3 powder containing 0, 4 and 8 wt. % CNTs. An empirical model using Pareto diagram was developed to link plasma processing parameters with the porosity of coating. Splat morphology as a function of plasma processing parameter was also studied to understand its effect on mechanical properties. Addition of a mere 1.5 wt. % CNTs via CVD technique showed ∼27% and ∼24% increase in the elastic modulus and fracture toughness respectively. Improved toughness was attributed to combined effect of lower porosity and uniform dispersion of CNTs which promoted the toughening by CNT bridging, crack deflection and strong CNT/Al2O3 interface. Al2O 3-8 wt. % CNT coating synthesized using spray dried powder showed 73% improvement in the fracture toughness when porosity reduced from 4.7% to 3.0%. Wear resistance of all coatings at room and elevated temperatures (573 K, 873 K) showed improvement with CNT addition and decreased porosity. Such behavior was due to improved mechanical properties, protective film formation due to tribochemical reaction, and CNT bridging between the splats. Finally, process maps correlating porosity content, CNT content, mechanical properties, and wear properties were developed.

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Tropical Cyclones are a continuing threat to life and property. Willoughby (2012) found that a Pareto (power-law) cumulative distribution fitted to the most damaging 10% of US hurricane seasons fit their impacts well. Here, we find that damage follows a Pareto distribution because the assets at hazard follow a Zipf distribution, which can be thought of as a Pareto distribution with exponent 1. The Z-CAT model is an idealized hurricane catastrophe model that represents a coastline where populated places with Zipf- distributed assets are randomly scattered and damaged by virtual hurricanes with sizes and intensities generated through a Monte-Carlo process. Results produce realistic Pareto exponents. The ability of the Z-CAT model to simulate different climate scenarios allowed testing of sensitivities to Maximum Potential Intensity, landfall rates and building structure vulnerability. The Z-CAT model results demonstrate that a statistical significant difference in damage is found when only changes in the parameters create a doubling of damage.