IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i17p9518-d620780.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/17/9518/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/17/9518/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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, January.
    10. John N. Hooker, 2012. "Integrated Methods for Optimization," International Series in Operations Research and Management Science, Springer, number 978-1-4614-1900-6, April.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jain, A. S. & Meeran, S., 1999. "Deterministic job-shop scheduling: Past, present and future," European Journal of Operational Research, Elsevier, vol. 113(2), pages 390-434, March.
    2. F. Guerriero, 2008. "Hybrid Rollout Approaches for the Job Shop Scheduling Problem," Journal of Optimization Theory and Applications, Springer, vol. 139(2), pages 419-438, November.
    3. Da Col, Giacomo & Teppan, Erich C., 2022. "Industrial-size job shop scheduling with constraint programming," Operations Research Perspectives, Elsevier, vol. 9(C).
    4. P M E Shutler, 2003. "A priority list based heuristic for the job shop problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(6), pages 571-584, June.
    5. Noordhoek, Marije & Dullaert, Wout & Lai, David S.W. & de Leeuw, Sander, 2018. "A simulation–optimization approach for a service-constrained multi-echelon distribution network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 292-311.
    6. Groflin, Heinz & Klinkert, Andreas, 2007. "Feasible insertions in job shop scheduling, short cycles and stable sets," European Journal of Operational Research, Elsevier, vol. 177(2), pages 763-785, March.
    7. Zheng, Liang & Xue, Xinfeng & Xu, Chengcheng & Ran, Bin, 2019. "A stochastic simulation-based optimization method for equitable and efficient network-wide signal timing under uncertainties," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 287-308.
    8. Drexl, Andreas & Kolisch, Rainer, 1991. "Produktionsplanung und -steuerung bei Einzel- und Kleinserienfertigung," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 281, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    9. Sündüz Dağ, 2013. "An Application On Flowshop Scheduling," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 1(1), pages 47-56, December.
    10. Helena Ramalhinho-Lourenço & Olivier C. Martin & Thomas Stützle, 2000. "Iterated local search," Economics Working Papers 513, Department of Economics and Business, Universitat Pompeu Fabra.
    11. Guanxiao Qi & Hongbin Huang & Haijun Wang, 2007. "Size Instabilities In The Ring And Linear Arrays Of Chaotic Systems," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 10(03), pages 301-313.
    12. Roberto Aringhieri & Giuliana Carello & Daniela Morale, 2016. "Supporting decision making to improve the performance of an Italian Emergency Medical Service," Annals of Operations Research, Springer, vol. 236(1), pages 131-148, January.
    13. Jianxin Fang & Brenda Cheang & Andrew Lim, 2023. "Problems and Solution Methods of Machine Scheduling in Semiconductor Manufacturing Operations: A Survey," Sustainability, MDPI, vol. 15(17), pages 1-44, August.
    14. Warren B. Powell, 2010. "Rejoinder ---The Languages of Stochastic Optimization," INFORMS Journal on Computing, INFORMS, vol. 22(1), pages 23-25, February.
    15. Albert Corominas & Alberto García-Villoria & Rafael Pastor, 2013. "Metaheuristic algorithms hybridised with variable neighbourhood search for solving the response time variability problem," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(2), pages 296-312, July.
    16. Anurag Agarwal & Varghese S. Jacob & Hasan Pirkul, 2006. "An Improved Augmented Neural-Network Approach for Scheduling Problems," INFORMS Journal on Computing, INFORMS, vol. 18(1), pages 119-128, February.
    17. Binzi Xu & Kai Xu & Baolin Fei & Dengchao Huang & Liang Tao & Yan Wang, 2024. "Automatic Design of Energy-Efficient Dispatching Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling Based on Dual Feature Weight Sets," Mathematics, MDPI, vol. 12(10), pages 1-24, May.
    18. Parlakturk, Ali & Kumar, Sunil, 2004. "Self-Interested Routing in Queueing Networks," Research Papers 1782r, Stanford University, Graduate School of Business.
    19. Brammer, Janis & Lutz, Bernhard & Neumann, Dirk, 2022. "Permutation flow shop scheduling with multiple lines and demand plans using reinforcement learning," European Journal of Operational Research, Elsevier, vol. 299(1), pages 75-86.
    20. Jacomine Grobler & Andries Engelbrecht & Schalk Kok & Sarma Yadavalli, 2010. "Metaheuristics for the multi-objective FJSP with sequence-dependent set-up times, auxiliary resources and machine down time," Annals of Operations Research, Springer, vol. 180(1), pages 165-196, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:17:p:9518-:d:620780. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.