Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "Rosienkiewicz, Maria" wg kryterium: Autor

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-4 z 4
    Tytuł:
    Artificial Intelligence Methods in Spare Parts Demand Forecasting
    Autorzy:
    Rosienkiewicz, Maria
    Powiązania:
    https://bibliotekanauki.pl/articles/504136.pdf
    Data publikacji:
    2013
    Wydawca:
    Międzynarodowa Wyższa Szkoła Logistyki i Transportu
    Tematy:
    lumpy demand
    forecasting
    ANN
    Artificial Intelligence
    Opis:
    The paper discusses the problem of forecasting lumpy demand which is typical for spare parts. Several prediction methods are presented in the article – traditional techniques based on time series and advanced methods that use Artificial Intelligence tools. The research conducted in the paper focuses on comparison of eight forecasting methods, including classical, hybrid and based on artificial neural networks. The aim of the paper is to assess the efficiency of lumpy demand forecasting methods that apply AI tools. The assessment is conducted by a comparison with traditional methods and it is based on Root Mean Square Errors (RMSE) and relative forecast errors (ex post) values. The article presents also a new approach to the lumpy demand forecasting issue – a method which combines regression modelling, information criteria and artificial neural networks.
    Źródło:
    Logistics and Transport; 2013, 18, 2; 41-50
    1734-2015
    Pojawia się w:
    Logistics and Transport
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Artificial intelligence-based hybrid forecasting models for manufacturing systems
    Autorzy:
    Rosienkiewicz, Maria
    Powiązania:
    https://bibliotekanauki.pl/articles/1841698.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    artificial neural network
    support vector machine
    extreme learning machine
    hybrid forecasting
    production planning
    maintenance
    quality control
    Opis:
    The paper addresses the problem of forecasting in manufacturing systems. The main aim of the research is to propose new hybrid forecasting models combining artificial intelligencebased methods with traditional techniques based on time series – namely: Hybrid econometric model, Hybrid artificial neural network model, Hybrid support vector machine model and Hybrid extreme learning machine model. The study focuses on solving the problem of limited access to independent variables. Empirical verification of the proposed models is built upon real data from the three manufacturing system areas – production planning, maintenance and quality control. Moreover, in the paper, an algorithm for the forecasting accuracy assessment and optimal method selection for industrial companies is introduced. It can serve not only as an efficient and costless tool for advanced manufacturing companies willing to select the right forecasting method for their particular needs but also as an approach supporting the initial steps of transformation towards smart factory and Industry 4.0 implementation.
    Źródło:
    Eksploatacja i Niezawodność; 2021, 23, 2; 263-277
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An Integrated Approach towards Building a Simulation Model Supporting the Management of the Passenger Transportation System. Part 2 – Case Study.
    Autorzy:
    Gąbka, Joanna
    Susz, Sławomir
    Rosienkiewicz, Maria
    Powiązania:
    https://bibliotekanauki.pl/articles/503826.pdf
    Data publikacji:
    2016
    Wydawca:
    Międzynarodowa Wyższa Szkoła Logistyki i Transportu
    Tematy:
    decision supporting tool
    transportation system
    algorithm verification
    simulation model
    Opis:
    This article presents a simulation model designated as an advising and forecasting tool for designing, redesigning and managing ground-based transportation systems. It considers both public and private transport means. It enables visualisation of the results of changes in the transportation network such as a new transportation mode, schedule adjustment, technology improvements on shuttle speed and other modifications that can influence the effectiveness of the transportation network. The simulation tool enables predictions of future passenger flow size for different means of transport. The simulation tool was developed after thorough analysis of interdependencies between variables in the transportation network model built upon an econometric model, artificial neural network and mathematical model. The simulation model was tested on the real data and determined to be very effective, useful and flexible in use. Successive phases of the model development proved that development of a reliable advising and forecasting tool requires a combination of different methods.
    Źródło:
    Logistics and Transport; 2016, 32, 4; 51-64
    1734-2015
    Pojawia się w:
    Logistics and Transport
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An Integrated Approach towards Building a Simulation Model Supporting the Management of the Passenger Transportation System. Part 1 - Theoretical basis.
    Autorzy:
    Gąbka, Joanna
    Susz, Sławomir
    Rosienkiewicz, Maria
    Powiązania:
    https://bibliotekanauki.pl/articles/504202.pdf
    Data publikacji:
    2016
    Wydawca:
    Międzynarodowa Wyższa Szkoła Logistyki i Transportu
    Tematy:
    integrated approach
    management system
    transport network
    algorithm components
    simulation model
    Opis:
    This article presents an integrated approach towards building a simulation model of the transportation network. The proposed method is based on the recommendations of the Blue Book for Sector of Public Transport in cities, agglomerations and regions issued by Jaspers (Joint Assistance to Support Projects in European Regions). It comprises six steps with such elements as econometric modelling, artificial neural networks and mathematical model. It is dedicated to developing simulation models for the purpose of picturing present situation and dependencies dominating in the transportation network, easily reflecting effects of potential modifications and providing forecasts for the future. The described hybrid method was verified in practice by building the simulation model which is presented in the next article in this series entitled: An Integrated Approach towards Building a Simulation Model Supporting the Management of the Passenger Transportation System. Part 2 – Case Study.
    Źródło:
    Logistics and Transport; 2016, 32, 4; 39-50
    1734-2015
    Pojawia się w:
    Logistics and Transport
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

      Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies