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Modelling the Reliability of Logistics Flows in a Complex Production System

Author

Listed:
  • Bożena Zwolińska

    (Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, 30-059 Krakow, Poland)

  • Jakub Wiercioch

    (Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, 30-059 Krakow, Poland)

Abstract

This paper analyses the disruptions occurring in a production system determining the operating states of a single machine. A system with a convergent production character, in which both single flows (streams) and multi-stream flows occur, was considered. In this paper, a two-level formalisation of the production system (PS) was made according to complex systems theory. The continuity analysis was performed at the operational level (manufacturing machine level). The definition of the k th survival value and the quasi-coherence property defined on chains of synchronous relations were used to determine the impact of interruption of the processed material flow on uninterrupted machine operation. The developed methodology is presented in terms of shaping the energy efficiency of technical objects with the highest power demand (the furnace of an automatic paint shop and the furnace of a glass tempering line were taken into consideration). The proposed methodology is used to optimise energy consumption in complex production structures. The model presented is utilitarian in nature—it can be applied to any technical system where there is randomness of task execution times and randomness of unplanned events. This paper considers the case in which two mutually independent random variables determining the duration of correct operation T P and the duration of breakdown T B are determined by a given distribution: Gaussian and Gamma family distributions (including combinations of exponential and Erlang distributions). A formalised methodology is also developed to determine the stability of system operation, as well as to assess the potential risk for arbitrary system evaluation parameters.

Suggested Citation

  • Bożena Zwolińska & Jakub Wiercioch, 2023. "Modelling the Reliability of Logistics Flows in a Complex Production System," Energies, MDPI, vol. 16(24), pages 1-22, December.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:24:p:8071-:d:1300475
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    References listed on IDEAS

    as
    1. Diana Filipe & Carina Pimentel, 2023. "Production and Internal Logistics Flow Improvements through the Application of Total Flow Management," Logistics, MDPI, vol. 7(2), pages 1-22, June.
    2. Büchi, Giacomo & Cugno, Monica & Castagnoli, Rebecca, 2020. "Smart factory performance and Industry 4.0," Technological Forecasting and Social Change, Elsevier, vol. 150(C).
    3. Sven-Vegard Buer & Marco Semini & Jan Ola Strandhagen & Fabio Sgarbossa, 2021. "The complementary effect of lean manufacturing and digitalisation on operational performance," International Journal of Production Research, Taylor & Francis Journals, vol. 59(7), pages 1976-1992, April.
    4. Kristina Höse & Afonso Amaral & Uwe Götze & Paulo Peças, 2023. "Manufacturing Flexibility through Industry 4.0 Technological Concepts—Impact and Assessment," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(2), pages 271-289, June.
    5. Vandana & Shiv Raj Singh & Mitali Sarkar & Biswajit Sarkar, 2023. "Effect of Learning and Forgetting on Inventory Model under Carbon Emission and Agile Manufacturing," Mathematics, MDPI, vol. 11(2), pages 1-20, January.
    6. Miroshnychenko, Ivan & Strobl, Andreas & Matzler, Kurt & De Massis, Alfredo, 2021. "Absorptive capacity, strategic flexibility, and business model innovation: Empirical evidence from Italian SMEs," Journal of Business Research, Elsevier, vol. 130(C), pages 670-682.
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