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Innovative System for Scheduling Production Using a Combination of Parametric Simulation Models

Author

Listed:
  • Branislav Micieta

    (Faculty of Mechanical Engineering, University of Žilina, 010 26 Zilina, Slovakia)

  • Jolanta Staszewska

    (Institute of Management and Quality Sciences, Humanitas University in Sosnowiec, 41-200 Sosnowiec, Poland)

  • Matej Kovalsky

    (Faculty of Mechanical Engineering, University of Žilina, 010 26 Zilina, Slovakia)

  • Martin Krajcovic

    (Faculty of Mechanical Engineering, University of Žilina, 010 26 Zilina, Slovakia)

  • Vladimira Binasova

    (Faculty of Mechanical Engineering, University of Žilina, 010 26 Zilina, Slovakia)

  • Ladislav Papanek

    (Faculty of Mechanical Engineering, University of Žilina, 010 26 Zilina, Slovakia)

  • Ivan Antoniuk

    (Faculty of Mechanical Engineering, University of Žilina, 010 26 Zilina, Slovakia)

Abstract

The article deals with the design of an innovative system for scheduling piece and small series discrete production using a combination of parametric simulation models and selected optimization methods. An innovative system for solving production scheduling problems is created based on data from a real production system at the workshop level. The methodology of the innovative system using simulation and optimization methods deals with the sequential scheduling problem due to its versatility, which includes several production systems and due to the fact that in practice, several modifications to production scheduling problems are encountered. Proposals of individual modules of the innovative system with the proposed communication channels have been presented, which connect the individual elements of the created library of objects for solving problems of sequential production scheduling. With the help of created communication channels, it is possible to apply individual parameters of a real production system directly to the assembled simulation model. In this system, an initial set of optimization methods is deployed, which can be applied to solve the sequential problem of production scheduling. The benefit of the solution is an innovative system that defines the content of the necessary data for working with the innovative system and the design of output reports that the proposed system provides for production planning for the production shopfloor level. The DPSS system works with several optimization methods (CR—Critical Ratio, S/RO—Slack/Remaining Operations, FDD—Flow Due Date, MWKR—Most Work Remaining, WSL—Waiting Slack, OPFSLK/PK—Operational Flow Slack per Processing Time) and the simulation experiments prove that the most suitable solution for the FT10 problem is the critical ratio method in which the replaceability of the equipment was not considered. The total length of finding all solutions by the DPSS system was 1.68 min. The main benefit of the DPSS system is the combination of two effectively used techniques not only in practice, but also in research; the mentioned techniques are production scheduling and discrete computer simulation. By combining techniques, it is possible to generate a dynamically and interactively changing simulated production program. Subsequently, it is possible to decide in the emerging conditions of certainty, uncertainty, but also risk. To determine the conditions, models of production systems are used, which represent physical production systems with their complex internal processes. Another benefit of combining techniques is the ability to evaluate a production system with a number of emerging problem modifications.

Suggested Citation

  • Branislav Micieta & Jolanta Staszewska & Matej Kovalsky & Martin Krajcovic & Vladimira Binasova & Ladislav Papanek & Ivan Antoniuk, 2021. "Innovative System for Scheduling Production Using a Combination of Parametric Simulation Models," Sustainability, MDPI, vol. 13(17), pages 1-20, August.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:17:p:9518-:d:620780
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    References listed on IDEAS

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    1. Michael C. Fu, 2002. "Feature Article: Optimization for simulation: Theory vs. Practice," INFORMS Journal on Computing, INFORMS, vol. 14(3), pages 192-215, August.
    2. Fabio Fruggiero & Marcello Fera & Alfredo Lambiase & Giada Martino & Maria Elena Nenni, 2013. "Production Scheduling Approaches for Operations Management," Chapters, in: Massimiliano M. Schiraldi (ed.), Operations Management, IntechOpen.
    3. Tasgetiren, M. Fatih & Liang, Yun-Chia & Sevkli, Mehmet & Gencyilmaz, Gunes, 2007. "A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1930-1947, March.
    4. S. S. Panwalkar & Wafik Iskander, 1977. "A Survey of Scheduling Rules," Operations Research, INFORMS, vol. 25(1), pages 45-61, February.
    5. Kumar Abhishek & Sven Leyffer & Jeff Linderoth, 2010. "FilMINT: An Outer Approximation-Based Solver for Convex Mixed-Integer Nonlinear Programs," INFORMS Journal on Computing, INFORMS, vol. 22(4), pages 555-567, November.
    6. R. J. M. Vaessens & E. H. L. Aarts & J. K. Lenstra, 1996. "Job Shop Scheduling by Local Search," INFORMS Journal on Computing, INFORMS, vol. 8(3), pages 302-317, August.
    7. Jacob Lohmer & Rainer Lasch, 2021. "Production planning and scheduling in multi-factory production networks: a systematic literature review," International Journal of Production Research, Taylor & Francis Journals, vol. 59(7), pages 2028-2054, April.
    8. Haifei Yu & Songjian Han & Dongsheng Yang & Zhiyong Wang & Wei Feng & Atila Bueno, 2021. "Job Shop Scheduling Based on Digital Twin Technology: A Survey and an Intelligent Platform," Complexity, Hindawi, vol. 2021, pages 1-12, April.
    9. Peter Brucker, 2007. "Scheduling Algorithms," Springer Books, Springer, edition 0, number 978-3-540-69516-5, December.
    10. John N. Hooker, 2012. "Integrated Methods for Optimization," International Series in Operations Research and Management Science, Springer, number 978-1-4614-1900-6, December.
    11. Tseng, Fan T. & Stafford, Edward F. & Gupta, Jatinder N. D., 2004. "An empirical analysis of integer programming formulations for the permutation flowshop," Omega, Elsevier, vol. 32(4), pages 285-293, August.
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    Cited by:

    1. Jana Stofkova & Matej Krejnus & Katarina Repkova Stofkova & Peter Malega & Vladimira Binasova, 2022. "Use of the Analytic Hierarchy Process and Selected Methods in the Managerial Decision-Making Process in the Context of Sustainable Development," Sustainability, MDPI, vol. 14(18), pages 1-20, September.
    2. Miroslav Rakyta & Peter Bubenik & Vladimira Binasova & Branislav Micieta & Katarina Staffenova, 2022. "Advanced Logistics Strategy of a Company to Create Sustainable Development in the Industrial Area," Sustainability, MDPI, vol. 14(19), pages 1-36, October.
    3. Peter Bubenik & Juraj Capek & Miroslav Rakyta & Vladimira Binasova & Katarina Staffenova, 2022. "Impact of Strategy Change on Business Process Management," Sustainability, MDPI, vol. 14(17), pages 1-23, September.

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