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A digital twins concept model for integrated maintenance: a case study for crane operation

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  • Janusz Szpytko

    (AGH University of Science and Technology)

  • Yorlandys Salgado Duarte

    (AGH University of Science and Technology)

Abstract

The paper presents an Integrated Maintenance Decision Making Model (IMDMM) concept for cranes under operation especially into the container type terminals. The target is to improve cranes operational efficiency through minimizing the risk of the Gantry Cranes Inefficiency (GCI) results based on the implementation of the Digital Twins concept for maintenance purposes. The proposed model makes a joint transportation process and crane maintenance scheduling, relevant to assure more robust performances in stochastic environments, as well as to assess and optimize performances at different levels, from components and transport device to production systems (container terminal). The crane operation risk is estimated with a sequential Markov chain Monte Carlo simulation model and the optimization model behind of IMDMM is supported through the Particle Swarm Optimization algorithms because the objective function a non-linear stochastics problem with bounded constrains. The developed model allows the container terminal operators (management process) to obtain a maintenance schedule that minimizes the GCI (holistic indicator), as well as establishing the desired level of risk. The paper demonstrates the effectiveness of the proposed maintenance decision making concept model for cranes under operation using data from of a real container terminal (case study).

Suggested Citation

  • Janusz Szpytko & Yorlandys Salgado Duarte, 2021. "A digital twins concept model for integrated maintenance: a case study for crane operation," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1863-1881, October.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:7:d:10.1007_s10845-020-01689-5
    DOI: 10.1007/s10845-020-01689-5
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    References listed on IDEAS

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    1. Baniasadi, Pouya & Foumani, Mehdi & Smith-Miles, Kate & Ejov, Vladimir, 2020. "A transformation technique for the clustered generalized traveling salesman problem with applications to logistics," European Journal of Operational Research, Elsevier, vol. 285(2), pages 444-457.
    2. Zhi Li & Ali Vatankhah Barenji & Jiazhi Jiang & Ray Y. Zhong & Gangyan Xu, 2020. "A mechanism for scheduling multi robot intelligent warehouse system face with dynamic demand," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 469-480, February.
    3. Jalel Euchi & Riadh Moussi & Fatma Ndiaye & Adnan Yassine, 2016. "Ant Colony Optimization for Solving the Container Stacking Problem: Case of Le Havre (France) Seaport Terminal," International Journal of Applied Logistics (IJAL), IGI Global, vol. 6(2), pages 81-101, July.
    4. Wenchang Luo & Yao Xu & Weitian Tong & Guohui Lin, 2019. "Single-machine scheduling with job-dependent machine deterioration," Journal of Scheduling, Springer, vol. 22(6), pages 691-707, December.
    5. Mehdi Foumani & Asghar Moeini & Michael Haythorpe & Kate Smith-Miles, 2018. "A cross-entropy method for optimising robotic automated storage and retrieval systems," International Journal of Production Research, Taylor & Francis Journals, vol. 56(19), pages 6450-6472, October.
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    Cited by:

    1. Aleksandra Bartosiewicz & Adam Kucharski, 2024. "The reloading of a ship in a maritime container terminal as a queuing problem of interacting processes," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 34(1), pages 17-33.
    2. Julian Neugebauer & Leonard Heilig & Stefan Voß, 2024. "Digital Twins in the Context of Seaports and Terminal Facilities," Flexible Services and Manufacturing Journal, Springer, vol. 36(3), pages 821-917, September.

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