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    Wyświetlanie 1-3 z 3
    Tytuł:
    Predictive Business Process Monitoring with Tree-based Classification Algorithms
    Autorzy:
    Owczarek, Tomasz
    Janke, Piotr
    Powiązania:
    https://bibliotekanauki.pl/articles/503954.pdf
    Data publikacji:
    2018
    Wydawca:
    Międzynarodowa Wyższa Szkoła Logistyki i Transportu
    Tematy:
    business process
    prediction
    classification
    random forest
    gradient boosting
    Opis:
    Predictive business process monitoring is a current research area which purpose is to predict the outcome of a whole process (or an element of a process i.e. a single event or task) based on available data. In the article we explore the possibility of use of the machine learning classification algorithms based on trees (CART, C5.0, random forest and extreme gradient boosting) in order to anticipate the result of a process. We test the application of these algorithms on real world event-log data and compare it with the known approaches. Our results show that.
    Źródło:
    Logistics and Transport; 2018, 40, 4; 73-82
    1734-2015
    Pojawia się w:
    Logistics and Transport
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Testing algorithms for quick rescheduling flow shop problems with FlexSim based simulation and R engine
    Autorzy:
    Janke, Piotr
    Powiązania:
    https://bibliotekanauki.pl/articles/27313435.pdf
    Data publikacji:
    2023
    Wydawca:
    Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
    Tematy:
    flow-shop problem
    genetic algorithm
    simulation
    problem przepływowy
    algorytm genetyczny
    symulacja
    Opis:
    Purpose: The aim of this paper is to present a combination of advanced algorithms for finding optimal solutions together with their tests for a permutation flow-shop problem with the possibilities offered by a simulation environment. Four time-constrained algorithms are tested and compared for a specific problem. Design/methodology/approach: Four time-constrained algorithms are tested and compared for a specific problem. The results of the work realisation of the algorithms are transferred to a simulation environment. The entire solution proposed in the work is composed as a parallel environment to the real implementation of the production process. Findings: The genetic algorithm generated the best solution in the same specified short time. By implementing the adopted approach, the correct cooperation of the FlexSim simulation environment with the R language engine was obtained. Research limitations/implications: The genetic algorithm generated the best solution in the same specified short time. By implementing the approach, a correct interaction between the FlexSim simulation environment and the R language engine was achieved. Practical implications: The solution proposed in this paper can be used as an environment to test solutions proposed in production. Simulation methods in the areas of logistics and production have for years attracted the interest of the scientific community and the wider industry. Combining the achievements of science in solving computationally complex problems with increasingly sophisticated algorithms, including artificial intelligence algorithms, with simulation methods that allow a detailed overview of the consequences of changes made seems promising. Originality/value: The original concept of cooperation between the R environment and the FlexSim simulation software for a specific problem was presented.
    Źródło:
    Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska; 2023, 168; 163--175
    1641-3466
    Pojawia się w:
    Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Utilisation of the artificial neural network in the strategy for the allocation of storage space
    Autorzy:
    Janke, Piotr
    Jończyk, Paweł
    Powiązania:
    https://bibliotekanauki.pl/articles/1883695.pdf
    Data publikacji:
    2020
    Wydawca:
    Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
    Tematy:
    logistics
    machine learning
    artificial intelligence
    neural networks
    logistyka
    uczenie maszynowe
    sztuczna inteligencja
    sieci neuronowe
    Opis:
    Purpose: The main goal of the article is to develop a method that automatically allocates the warehouse zones of the product range of the studied enterprise for the selected machine learning algorithm. Design/methodology/approach: The problem of the studied issue is presented in the context of a specific company. The research used the double ABC method for the initial classification of zones. Input data were prepared according to the developed methodology. Selected machine learning algorithms were tested for the same data. Findings: Machine learning methods can be used to classify storage zones in that specific warehouse. Especially Boosted Trees and Neural Networks gives small errors at training stage witch our methodology. There may be differences in errors at the stage of learning the algorithm and the stage of implementing it with completely new data. Originality/value: Machine learning is a new solution that is increasingly used in various areas of logistics. The article draws attention to some problems in implementing this solution for enterprises.
    Źródło:
    Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska; 2020, 145; 197-209
    1641-3466
    Pojawia się w:
    Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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