IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v209y2013i1p41-6510.1007-s10479-011-0868-5.html
   My bibliography  Save this article

Discrete time model for two-machine one-buffer transfer lines with restart policy

Author

Listed:
  • Elisa Gebennini
  • Andrea Grassi
  • Cesare Fantuzzi
  • Stanley Gershwin
  • Irvin Schick

Abstract

The paper deals with analytical modeling of transfer lines consisting of two machines decoupled by one finite buffer. In particular, the case in which a control policy (referred as “restart policy”) aiming to reduce the blocking frequency of the first machine is addressed. Such a policy consists of forcing the first machine to remain idle (it cannot process parts) each time the buffer gets full until it empties again. This specific behavior can be found in a number of industrial production systems, especially when some machines are affected by outage costs when stops occur. The two-machine one-buffer line is here modeled as a discrete time Markov process and the two machines are characterized by the same operation time. The analytical solution of the model is obtained and mathematical expressions of the most important performance measures are provided. Some significant remarks about the effect of the proposed restart policy on the behavior of the system are also pointed out. Copyright Springer Science+Business Media, LLC 2013

Suggested Citation

  • Elisa Gebennini & Andrea Grassi & Cesare Fantuzzi & Stanley Gershwin & Irvin Schick, 2013. "Discrete time model for two-machine one-buffer transfer lines with restart policy," Annals of Operations Research, Springer, vol. 209(1), pages 41-65, October.
  • Handle: RePEc:spr:annopr:v:209:y:2013:i:1:p:41-65:10.1007/s10479-011-0868-5
    DOI: 10.1007/s10479-011-0868-5
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10479-011-0868-5
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s10479-011-0868-5?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. Lutz, Christian M. & Roscoe Davis, K. & Sun, Minghe, 1998. "Determining buffer location and size in production lines using tabu search," European Journal of Operational Research, Elsevier, vol. 106(2-3), pages 301-316, April.
    2. Heavey, C. & Papadopoulos, H. T. & Browne, J., 1993. "The throughput rate of multistation unreliable production lines," European Journal of Operational Research, Elsevier, vol. 68(1), pages 69-89, July.
    3. 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.
    4. Berman, Oded, 1982. "Efficiency and production rate of a transfer line with two machines and a finite storage buffer," European Journal of Operational Research, Elsevier, vol. 9(3), pages 295-308, March.
    5. 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.
    6. Papadopoulos, H. T. & Vidalis, M. I., 2001. "Minimizing WIP inventory in reliable production lines," International Journal of Production Economics, Elsevier, vol. 70(2), pages 185-197, March.
    7. Stanley B. Gershwin, 1987. "An Efficient Decomposition Method for the Approximate Evaluation of Tandem Queues with Finite Storage Space and Blocking," Operations Research, INFORMS, vol. 35(2), pages 291-305, April.
    8. Diomidis Spinellis & Chrissoleon Papadopoulos, 2000. "A simulated annealing approach for buffer allocation in reliable production lines," Annals of Operations Research, Springer, vol. 93(1), pages 373-384, January.
    9. Stanley Gershwin & Mitchell Burman, 2000. "A decomposition method for analyzing inhomogeneous assembly/disassembly systems," Annals of Operations Research, Springer, vol. 93(1), pages 91-115, January.
    10. Hong, Yushin & Seong, Deokhyun, 1993. "The analysis of an unreliable two-machine production line with random processing times," European Journal of Operational Research, Elsevier, vol. 68(2), pages 228-235, July.
    11. Stanley Gershwin & James Schor, 2000. "Efficient algorithms for buffer space allocation," Annals of Operations Research, Springer, vol. 93(1), pages 117-144, January.
    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. Kiesmüller, G.P. & Sachs, F.E., 2020. "Spare parts or buffer? How to design a transfer line with unreliable machines," European Journal of Operational Research, Elsevier, vol. 284(1), pages 121-134.

    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. Elisa Gebennini & Andrea Grassi & Cesare Fantuzzi, 2015. "The two-machine one-buffer continuous time model with restart policy," Annals of Operations Research, Springer, vol. 231(1), pages 33-64, August.
    2. 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.
    3. Konstantinos S. Boulas & Georgios D. Dounias & Chrissoleon T. Papadopoulos, 2023. "A hybrid evolutionary algorithm approach for estimating the throughput of short reliable approximately balanced production lines," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 823-852, February.
    4. Chen, Chin-Tai & Yuan, John, 2004. "Transient throughput analysis for a series type system of machines in terms of alternating renewal processes," European Journal of Operational Research, Elsevier, vol. 155(1), pages 178-197, May.
    5. Shi, Chuan & Gershwin, Stanley B., 2009. "An efficient buffer design algorithm for production line profit maximization," International Journal of Production Economics, Elsevier, vol. 122(2), pages 725-740, December.
    6. Michael Manitz, 2015. "Analysis of assembly/disassembly queueing networks with blocking after service and general service times," Annals of Operations Research, Springer, vol. 226(1), pages 417-441, March.
    7. Sachs, F.E. & Helber, S. & Kiesmüller, G.P., 2022. "Evaluation of Unreliable Flow Lines with Limited Buffer Capacities and Spare Part Provisioning," European Journal of Operational Research, Elsevier, vol. 302(2), pages 544-559.
    8. Nahas, Nabil & Ait-Kadi, Daoud & Nourelfath, Mustapha, 2006. "A new approach for buffer allocation in unreliable production lines," International Journal of Production Economics, Elsevier, vol. 103(2), pages 873-881, October.
    9. Beixin Xia & Binghai Zhou & Ci Chen & Lifeng Xi, 2016. "A generalized-exponential decomposition method for the analysis of inhomogeneous assembly/disassembly systems with unreliable machines and finite buffers," Journal of Intelligent Manufacturing, Springer, vol. 27(4), pages 765-779, August.
    10. 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.
    11. Eva K. Lee & Siddhartha Maheshwary & Jacquelyn Mason & William Glisson, 2006. "Large-Scale Dispensing for Emergency Response to Bioterrorism and Infectious-Disease Outbreak," Interfaces, INFORMS, vol. 36(6), pages 591-607, December.
    12. 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.
    13. Juliane Müller & Christine Shoemaker & Robert Piché, 2014. "SO-I: a surrogate model algorithm for expensive nonlinear integer programming problems including global optimization applications," Journal of Global Optimization, Springer, vol. 59(4), pages 865-889, August.
    14. George Liberopoulos & George Kozanidis & Panagiotis Tsarouhas, 2007. "Performance Evaluation of an Automatic Transfer Line with WIP Scrapping During Long Failures," Manufacturing & Service Operations Management, INFORMS, vol. 9(1), pages 62-83, December.
    15. Papadopoulos, H. T. & Vidalis, M. I., 2001. "Minimizing WIP inventory in reliable production lines," International Journal of Production Economics, Elsevier, vol. 70(2), pages 185-197, March.
    16. Federico Nuñez-Piña & Joselito Medina-Marin & Juan Carlos Seck-Tuoh-Mora & Norberto Hernandez-Romero & Eva Selene Hernandez-Gress, 2018. "Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks," Complexity, Hindawi, vol. 2018, pages 1-10, January.
    17. Colledani, Marcello & Tolio, Tullio, 2009. "Performance evaluation of production systems monitored by statistical process control and off-line inspections," International Journal of Production Economics, Elsevier, vol. 120(2), pages 348-367, August.
    18. Belmansour, Ahmed-Tidjani & Nourelfath, Mustapha, 2010. "An aggregation method for performance evaluation of a tandem homogenous production line with machines having multiple failure modes," Reliability Engineering and System Safety, Elsevier, vol. 95(11), pages 1193-1201.
    19. Mehmet Savsar, 2016. "Reliability and availability analysis of a manufacturing line system," Journal of Applied and Physical Sciences, Prof. Vakhrushev Alexander, vol. 2(3), pages 96-106.
    20. Ziwei Lin & Nicla Frigerio & Andrea Matta & Shichang Du, 2021. "Multi-fidelity surrogate-based optimization for decomposed buffer allocation problems," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(1), pages 223-253, March.

    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:annopr:v:209:y:2013:i:1:p:41-65:10.1007/s10479-011-0868-5. 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.