Gestão de stocks aplicada a uma instituição de saúde privada

A gestão de stocks tem-se tornado uma ferramenta fundamental na racionalização de custos das empresas permitindo uma maior eficiência operacional. Os modelos de gestão de stocks procuram ajudar a determinar as quantidades a encomendar e quando encomendar, com um custo total de aprovisionamento mínimo. Este trabalho visa o estudo da gestão de stocks, dos modelos existentes e a aplicação de um deles num prestador de saúde, em particular, na área da imagiologia. Neste estudo optou-se por efectuar a classificação ABC dos produtos e posteriormente procurou-se definir o modelo de gestão de stocks que melhor se adeqúe à realidade empresarial em estudo de modo a manter o nível de stock correcto associado a um menor custo. Optou-se pelo modelo de revisão periódica de stocks que permite efectuar a encomenda sempre com o mesmo intervalo de tempo e efectuar ajustes nas quantidades necessárias. Com base nos dados fornecido efectuou-se uma previsão da procura e assim definiram-se as quantidades a encomendar, bem como o stock de segurança necessário. Implementaram-se as quantidades e o stock de segurança e efectuou-se uma avaliação dos resultados obtidos e analisou-se o impacto da gestão de stocks no prestador de saúde.

Otimização da atividade de picking num armazém de peças – um caso de estudo

Mestrado em Engenharia Mecânica – Gestão Industrial

Aplicação do ABC no sector de transporte público rodoviário de mercadorias: estudo de caso em PME

Mestrado em Contabilidade e Finanças Orientado por: Doutora Cláudia Lopes

Data mining techniques to support the classification of MV electricity customers

This paper describes a methodology that was developed for the classification of Medium Voltage (MV) electricity customers. Starting from a sample of data bases, resulting from a monitoring campaign, Data Mining (DM) techniques are used in order to discover a set of a MV consumer typical load profile and, therefore, to extract knowledge regarding to the electric energy consumption patterns. In first stage, it was applied several hierarchical clustering algorithms and compared the clustering performance among them using adequacy measures. In second stage, a classification model was developed in order to allow classifying new consumers in one of the obtained clusters that had resulted from the previously process. Finally, the interpretation of the discovered knowledge are presented and discussed.

Demand response programs definition supported by clustering and classification techniques

The growing importance and influence of new resources connected to the power systems has caused many changes in their operation. Environmental policies and several well know advantages have been made renewable based energy resources largely disseminated. These resources, including Distributed Generation (DG), are being connected to lower voltage levels where Demand Response (DR) must be considered too. These changes increase the complexity of the system operation due to both new operational constraints and amounts of data to be processed. Virtual Power Players (VPP) are entities able to manage these resources. Addressing these issues, this paper proposes a methodology to support VPP actions when these act as a Curtailment Service Provider (CSP) that provides DR capacity to a DR program declared by the Independent System Operator (ISO) or by the VPP itself. The amount of DR capacity that the CSP can assure is determined using data mining techniques applied to a database which is obtained for a large set of operation scenarios. The paper includes a case study based on 27,000 scenarios considering a diversity of distributed resources in a 33 bus distribution network.

A gestão de stocks do serviço de assistência técnica na área da eletromedicina

Dissertação apresentada ao Instituto Superior de Administração e Contabilidade do Porto para obtenção do Grau de Mestre em Logística Orientada por: Professora Doutora Maria Clara Rodrigues Bento Vaz Fernandes

Towards a classification scheme for musical sounds

Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), 2013

An automatic voice pleasantness classification system based on prosodic and acoustic patterns of voice preference

In the last few years the number of systems and devices that use voice based interaction has grown significantly. For a continued use of these systems the interface must be reliable and pleasant in order to provide an optimal user experience. However there are currently very few studies that try to evaluate how good is a voice when the application is a speech based interface. In this paper we present a new automatic voice pleasantness classification system based on prosodic and acoustic patterns of voice preference. Our study is based on a multi-language database composed by female voices. In the objective performance evaluation the system achieved a 7.3% error rate.