56 research outputs found

    Referenciação genérica: metodologia de caracterização de artigos

    Get PDF
    Fundação para a Ciência e a Tecnologia (FCT

    Benefícios e desafios da customização em massa

    Get PDF
    Fundação para a Ciência e a Tecnologia (FCT

    Distributed production planning and control agent based system

    Get PDF
    A model of an Agent based Production Planning and Control (PPC) system able to be dynamically adaptable to local and distributed utilization of production resources and materials is presented. The PPC system is based on the selection of resources to deal with one order of different quantities of one product each time. In this way it is build one scheduling solution for that particular order. The production resources are selected and scheduled using a multiagent system supported by an implementation of the Smith Contract Net, using Java Spaces technology. The multiagent system is based on three main agents: Client, Resource and Manager. These agents negotiate the final product, and the correspondent components, requested by the client. An order for each product (component) triggers a process of dynamic design of a production system to fulfill that particular order. This system exists till the end of the order

    Automatic simulation models generation of warehouses with milk runs and pickers

    Get PDF
    To help a company of the Bosch Group to reduce its costs (both in time and space) with its supermarket, a micro simulation model was developed in Simio. Particularly, the tool is able to model pickers riding milk runs to collect containers of products, from a supermarket, to satisfy the needs of the production lines. Practitionaer may benefit from this tool, since it is able to model different supermarket scenario, for instance different storage strategies. Additionally, the supermarket itself is automatically created, through an Add-in of Simio that was developed in C#, which implements the API of Simio. Conclusions and future work are discussed.This work has been co-supported by SI I&DT project in joint-promotion nº 36265/2013 (HMIEXCEL 2013-2015 Project) and by FCT Fundação para a Ciência e Tecnologia in the scope of the project: PEst-OE/EEI/UI0319/2014.info:eu-repo/semantics/publishedVersio

    Simulation model generation for warehouse management: Case study to test different storage strategies

    Get PDF
    A simulation model generator was developed to help a company of the Bosch Group to reduce costs in time and space with its warehouse. The simulation models are automatically created in Simio and can simulate pickers riding milk runs to collect containers from the warehouse, to satisfy the needs of production lines, assisting warehouse management by testing different storage strategies. Thus, the purpose of this paper is to present the developed generator and to use it in a case study, to test different storage policies for the company. The generator was validated, as was the simulation model automatically created. With this tool, some suggestions could be made to the company in question. Namely, it was shown that it would not be possible to maintain the FIFO rule and, at the same time, eliminate the upper floor of all racks for ergonomic reasons. To allow this, the rate of replacing containers should be synchronised with the needs of production lines. Alternatively, it was also shown that the performance of the warehouse would improve by dividing it into zones allocated to each milk run.This work has been supported by QREN: FCT – Fundação para a Ciência e Tecnologia within the Project Scope: PDE/BDE/114566/2016 – AESI: Advanced Engineering Systems for Industry doctoral programme

    Using simio to automatically create 3d warehouses and compare different storage strategies

    Get PDF
    This paper focuses on a simulation based approach to reduce warehouse costs. At an early stage, the tool needs to be able to generate different types of warehouses. To accomplish this, a Simio add-in was built in C#, using the Simio API, where the user only needs to insert the layout data on an excel spreadsheet. Afterwards, the created warehouse is capable of modelling different storage strategies and compare them. The obtained results indicate that the proposed strategy is able to reduce the picking time in about 15% and the number of stops per milk run in 50%. Moreover, it was found that the strategy currently in use needs 35% more space than the proposed one.This work has been co-supported by SI I&DT project in joint-promotion nº 36265/2013 (HMIEXCEL - 2013-2015 Project) and by FCT – Fundação para a Ciência e Tecnologia in the scope of the project: PEstOE/EEI/UI0319/2014.info:eu-repo/semantics/publishedVersio

    3D microsimulation of milkruns and pickers in warehouses using SIMIO

    Get PDF
    To help the Bosch Car Multimedia Portugal in Ferreiros, Braga to reduce its costs (both in time and space) with its warehouse, a micro simulation model is being developed in Simio. Particularly, the tool needs to be able to model pickers riding milkruns to collect containers of products, from a warehouse, to satisfy the needs of the production lines. In this sense, the storage strategy used on the warehouse, the quantity of requests a picker gets per shift, the time between shifts, the number of types of products, the arrival rate of requests, and the number of milkruns and pickers needs to be adjustable. Additionally, to design the corridors of the warehouse in a configurable way, an Add-in in C#, using the API of Simio, is being developed. Thus, this paper intends to document the first part of the simulation model developed, which consists on the pickers receiving requests and riding their milkruns to collect the respective containers from the warehouse. Five different Simio models compose the main simulation model. Conclusions and future work are discussed.This work has been co-supported by SI I&DT project in joint-promotion nº 36265/2013 (HMIEXCEL - 2013-2015 Project) and by FCT – Fundação para a Ciência e Tecnologia in the scope of the project: PEst-OE/EEI/UI0319/2014

    SARS-CoV-2 introductions and early dynamics of the epidemic in Portugal

    Get PDF
    Genomic surveillance of SARS-CoV-2 in Portugal was rapidly implemented by the National Institute of Health in the early stages of the COVID-19 epidemic, in collaboration with more than 50 laboratories distributed nationwide. Methods By applying recent phylodynamic models that allow integration of individual-based travel history, we reconstructed and characterized the spatio-temporal dynamics of SARSCoV-2 introductions and early dissemination in Portugal. Results We detected at least 277 independent SARS-CoV-2 introductions, mostly from European countries (namely the United Kingdom, Spain, France, Italy, and Switzerland), which were consistent with the countries with the highest connectivity with Portugal. Although most introductions were estimated to have occurred during early March 2020, it is likely that SARS-CoV-2 was silently circulating in Portugal throughout February, before the first cases were confirmed. Conclusions Here we conclude that the earlier implementation of measures could have minimized the number of introductions and subsequent virus expansion in Portugal. This study lays the foundation for genomic epidemiology of SARS-CoV-2 in Portugal, and highlights the need for systematic and geographically-representative genomic surveillance.We gratefully acknowledge to Sara Hill and Nuno Faria (University of Oxford) and Joshua Quick and Nick Loman (University of Birmingham) for kindly providing us with the initial sets of Artic Network primers for NGS; Rafael Mamede (MRamirez team, IMM, Lisbon) for developing and sharing a bioinformatics script for sequence curation (https://github.com/rfm-targa/BioinfUtils); Philippe Lemey (KU Leuven) for providing guidance on the implementation of the phylodynamic models; Joshua L. Cherry (National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health) for providing guidance with the subsampling strategies; and all authors, originating and submitting laboratories who have contributed genome data on GISAID (https://www.gisaid.org/) on which part of this research is based. The opinions expressed in this article are those of the authors and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services, or the United States government. This study is co-funded by Fundação para a Ciência e Tecnologia and Agência de Investigação Clínica e Inovação Biomédica (234_596874175) on behalf of the Research 4 COVID-19 call. Some infrastructural resources used in this study come from the GenomePT project (POCI-01-0145-FEDER-022184), supported by COMPETE 2020 - Operational Programme for Competitiveness and Internationalisation (POCI), Lisboa Portugal Regional Operational Programme (Lisboa2020), Algarve Portugal Regional Operational Programme (CRESC Algarve2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), and by Fundação para a Ciência e a Tecnologia (FCT).info:eu-repo/semantics/publishedVersio
    corecore