IDEAS home Printed from https://ideas.repec.org/a/spr/mathme/v83y2016i3d10.1007_s00186-015-0529-6.html
   My bibliography  Save this article

Approximations of time-dependent unreliable flow lines with finite buffers

Author

Listed:
  • S. Göttlich

    (University of Mannheim)

  • S. Kühn

    (University of Mannheim)

  • J. A. Schwarz

    (University of Mannheim)

  • R. Stolletz

    (University of Mannheim)

Abstract

Flow lines process discrete workpieces on consecutive machines, which are coupled by buffers. Their operating environment is often stochastic and time-dependent. For the flow line under consideration, the stochasticity is generated by random breakdowns and successive stochastic repair times, whereas the processing times are deterministic. However, the release rate of workpieces to the line is time-dependent, due to changes in demand. The buffers between the machines may be finite or infinite. We introduce two new sampling approaches for the performance evaluation of such flow lines: one method utilizes an approximation based on a mixed-integer program in discrete time with discrete material, while the other approximation is based on partial and ordinary differential equations in continuous time and with a continuous flow of material. In addition, we sketch a proof that these two approximations are equivalent under some linearity assumptions. A computational study demonstrates the accuracy of both approximations relative to a discrete-event simulation in continuous time. Furthermore, we reveal some effects occurring in unreliable flow lines with time-dependent processing rates.

Suggested Citation

  • S. Göttlich & S. Kühn & J. A. Schwarz & R. Stolletz, 2016. "Approximations of time-dependent unreliable flow lines with finite buffers," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 83(3), pages 295-323, June.
  • Handle: RePEc:spr:mathme:v:83:y:2016:i:3:d:10.1007_s00186-015-0529-6
    DOI: 10.1007/s00186-015-0529-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00186-015-0529-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s00186-015-0529-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Stefan Helber & Katja Schimmelpfeng & Raik Stolletz & Svenja Lagershausen, 2011. "Using linear programming to analyze and optimize stochastic flow lines," Annals of Operations Research, Springer, vol. 182(1), pages 193-211, January.
    2. Kan Wu & Leon McGinnis & Bert Zwart, 2011. "Queueing models for a single machine subject to multiple types of interruptions," IISE Transactions, Taylor & Francis Journals, vol. 43(10), pages 753-759.
    3. Stanley B. Gershwin & Irvin C. Schick, 1983. "Modeling and Analysis of Three-Stage Transfer Lines with Unreliable Machines and Finite Buffers," Operations Research, INFORMS, vol. 31(2), pages 354-380, April.
    4. Terwiesch, Christian & E. Bohn, Roger, 2001. "Learning and process improvement during production ramp-up," International Journal of Production Economics, Elsevier, vol. 70(1), pages 1-19, March.
    5. Jaikumar, Ramachandran & Bohn, Roger E., 1992. "A dynamic approach to operations management: An alternative to static optimization," International Journal of Production Economics, Elsevier, vol. 27(3), pages 265-282, October.
    6. Fan, Walter, 1976. "Simulation of queueing network with time varying arrival rates," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 18(3), pages 165-170.
    7. Wai Kin (Victor) Chan & Lee Schruben, 2008. "Optimization Models of Discrete-Event System Dynamics," Operations Research, INFORMS, vol. 56(5), pages 1218-1237, October.
    8. James S. Vandergraft, 1983. "A Fluid Flow Model of Networks of Queues," Management Science, INFORMS, vol. 29(10), pages 1198-1208, October.
    9. Takahashi, Katsuhiko & Nakamura, Nobuto, 2002. "Decentralized reactive Kanban system," European Journal of Operational Research, Elsevier, vol. 139(2), pages 262-276, June.
    10. Walid W. Nasr & Michael R. Taaffe, 2013. "Fitting the Ph t / M t / s / c Time-Dependent Departure Process for Use in Tandem Queueing Networks," INFORMS Journal on Computing, INFORMS, vol. 25(4), pages 758-773, November.
    11. Bariş Tan, 2015. "Mathematical programming representations of the dynamics of continuous-flow production systems," IISE Transactions, Taylor & Francis Journals, vol. 47(2), pages 173-189, February.
    Full references (including those not matched with items on IDEAS)

    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. George Liberopoulos, 2020. "Comparison of optimal buffer allocation in flow lines under installation buffer, echelon buffer, and CONWIP policies," Flexible Services and Manufacturing Journal, Springer, vol. 32(2), pages 297-365, June.
    2. Nadeau, Marie-Claude & Kar, Ashish & Roth, Richard & Kirchain, Randolph, 2010. "A dynamic process-based cost modeling approach to understand learning effects in manufacturing," International Journal of Production Economics, Elsevier, vol. 128(1), pages 223-234, November.
    3. Demeester, Lieven L. & Qi, Mei, 2005. "Managing learning resources for consecutive product generations," International Journal of Production Economics, Elsevier, vol. 95(2), pages 265-283, February.
    4. Khayyati, Siamak & Tan, Barış, 2020. "Data-driven control of a production system by using marking-dependent threshold policy," International Journal of Production Economics, Elsevier, vol. 226(C).
    5. Stefan Helber & Katja Schimmelpfeng & Raik Stolletz & Svenja Lagershausen, 2011. "Using linear programming to analyze and optimize stochastic flow lines," Annals of Operations Research, Springer, vol. 182(1), pages 193-211, January.
    6. Paulson Gjerde, Kathy A. & Slotnick, Susan A., 2004. "Quality and reputation: The effects of external and internal factors over time," International Journal of Production Economics, Elsevier, vol. 89(1), pages 1-20, May.
    7. Agathe Gilain & Pascal Le Masson & Benoit Weil, 2018. "Managing Learning Curves In The Unknown: From ‘Learning By Doing’ To ‘Learning By Designing’," Post-Print hal-01900961, HAL.
    8. Chame, Anna & Tsallis, Constantino, 1990. "Criticality of the discrete N-vector ferromagnet in a cubic lattice with a free surface," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 165(1), pages 41-63.
    9. Rossi, Tommaso & Pozzi, Rossella & Testa, Mariapaola, 2017. "EOQ-based inventory management in single-machine multi-item systems," Omega, Elsevier, vol. 71(C), pages 106-113.
    10. Alfieri, Arianna & Matta, Andrea, 2012. "Mathematical programming formulations for approximate simulation of multistage production systems," European Journal of Operational Research, Elsevier, vol. 219(3), pages 773-783.
    11. Arianna Alfieri & Andrea Matta & Giulia Pedrielli, 2015. "Mathematical programming models for joint simulation–optimization applied to closed queueing networks," Annals of Operations Research, Springer, vol. 231(1), pages 105-127, August.
    12. Leachman, Robert C. & Ding, Shengwei, 2007. "Integration of speed economics into decision-making for manufacturing management," International Journal of Production Economics, Elsevier, vol. 107(1), pages 39-55, May.
    13. Wu, Kan & McGinnis, Leon, 2012. "Performance evaluation for general queueing networks in manufacturing systems: Characterizing the trade-off between queue time and utilization," European Journal of Operational Research, Elsevier, vol. 221(2), pages 328-339.
    14. Li, Yuan & Wei, Zelong & Zhao, Jie & Zhang, Chenlu & Liu, Yi, 2013. "Ambidextrous organizational learning, environmental munificence and new product performance: Moderating effect of managerial ties in China," International Journal of Production Economics, Elsevier, vol. 146(1), pages 95-105.
    15. Manda, A.B. & Uzsoy, Reha, 2021. "Managing product transitions with learning and congestion effects," International Journal of Production Economics, Elsevier, vol. 239(C).
    16. Papadopoulos, H. T. & Heavey, C., 1996. "Queueing theory in manufacturing systems analysis and design: A classification of models for production and transfer lines," European Journal of Operational Research, Elsevier, vol. 92(1), pages 1-27, July.
    17. Thomke, Stefan H., 1998. "Simulation, learning and R&D performance: Evidence from automotive development," Research Policy, Elsevier, vol. 27(1), pages 55-74, May.
    18. Bonnin Roca, Jaime & O'Sullivan, Eoin, 2020. "Seeking coherence between barriers to manufacturing technology adoption and innovation policy," International Journal of Production Economics, Elsevier, vol. 230(C).
    19. Michael A. Lapré & Amit Shankar Mukherjee & Luk N. Van Wassenhove, 2000. "Behind the Learning Curve: Linking Learning Activities to Waste Reduction," Management Science, INFORMS, vol. 46(5), pages 597-611, May.
    20. Lauri Koskela & John Rooke & Mohan Siriwardena, 2016. "Evaluation of the Promotion of Through-Life Management in Public Private Partnerships for Infrastructure," Sustainability, MDPI, vol. 8(6), pages 1-23, June.

    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:spr:mathme:v:83:y:2016:i:3:d:10.1007_s00186-015-0529-6. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.