IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v144y2013i2p479-484.html
   My bibliography  Save this article

Influence of dispatching rules on average production lead time for multi-stage production systems

Author

Listed:
  • Hübl, Alexander
  • Jodlbauer, Herbert
  • Altendorfer, Klaus

Abstract

In this paper the influence of different dispatching rules on the average production lead time is investigated. Two theorems based on covariance between processing time and production lead time are formulated and proved theoretically. Theorem 1 links the average production lead time to the “processing time weighted production lead time” for the multi-stage production systems analytically. The influence of different dispatching rules on average lead time, which is well known from simulation and empirical studies, can be proved theoretically in Theorem 2 for a single stage production system. A simulation study is conducted to gain more insight into the influence of dispatching rules on average production lead time in a multi-stage production system. We find that the “processing time weighted average production lead time” for a multi-stage production system is not invariant of the applied dispatching rule and can be used as a dispatching rule independent indicator for single-stage production systems.

Suggested Citation

  • Hübl, Alexander & Jodlbauer, Herbert & Altendorfer, Klaus, 2013. "Influence of dispatching rules on average production lead time for multi-stage production systems," International Journal of Production Economics, Elsevier, vol. 144(2), pages 479-484.
    • Handle: RePEc:eee:proeco:v:144:y:2013:i:2:p:479-484
    DOI: 10.1016/j.ijpe.2013.03.020
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527313001369
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijpe.2013.03.020?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. Kleijnen, Jack P. C., 1995. "Verification and validation of simulation models," European Journal of Operational Research, Elsevier, vol. 82(1), pages 145-162, April.
    2. El-Bouri, Ahmed & Balakrishnan, Subramaniam & Popplewell, Neil, 2008. "Cooperative dispatching for minimizing mean flowtime in a dynamic flowshop," International Journal of Production Economics, Elsevier, vol. 113(2), pages 819-833, June.
    3. Chen, Binchao & Matis, Timothy I., 2013. "A flexible dispatching rule for minimizing tardiness in job shop scheduling," International Journal of Production Economics, Elsevier, vol. 141(1), pages 360-365.
    4. Jodlbauer, Herbert & Stocher, Wolfgang, 2006. "Little's Law in a continuous setting," International Journal of Production Economics, Elsevier, vol. 103(1), pages 10-16, September.
    5. Jodlbauer, Herbert, 2005. "Definition and properties of the input-weighted average lead-time," European Journal of Operational Research, Elsevier, vol. 164(2), pages 354-357, July.
    6. Jacek Błażewicz & Klaus H. Ecker & Erwin Pesch & Günter Schmidt & Jan Węglarz, 2007. "Handbook on Scheduling," International Handbooks on Information Systems, Springer, number 978-3-540-32220-7, December.
    7. Rajendran, Chandrasekharan & Holthaus, Oliver, 1999. "A comparative study of dispatching rules in dynamic flowshops and jobshops," European Journal of Operational Research, Elsevier, vol. 116(1), pages 156-170, July.
    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. Tanja Mlinar & Philippe Chevalier, 2016. "Pooling heterogeneous products for manufacturing environments," 4OR, Springer, vol. 14(2), pages 173-200, June.
    2. Jodlbauer, Herbert & Dehmer, Matthias & Strasser, Sonja, 2018. "A hybrid binomial inverse hypergeometric probability distribution: Theory and applications," Applied Mathematics and Computation, Elsevier, vol. 338(C), pages 44-54.

    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. A. S. Xanthopoulos & D. E. Koulouriotis, 2018. "Cluster analysis and neural network-based metamodeling of priority rules for dynamic sequencing," Journal of Intelligent Manufacturing, Springer, vol. 29(1), pages 69-91, January.
    2. Xiong, Hegen & Fan, Huali & Jiang, Guozhang & Li, Gongfa, 2017. "A simulation-based study of dispatching rules in a dynamic job shop scheduling problem with batch release and extended technical precedence constraints," European Journal of Operational Research, Elsevier, vol. 257(1), pages 13-24.
    3. Ferreira, Cristiane & Figueira, Gonçalo & Amorim, Pedro, 2022. "Effective and interpretable dispatching rules for dynamic job shops via guided empirical learning," Omega, Elsevier, vol. 111(C).
    4. Weibo Liu & Yan Jin & Mark Price, 2017. "New scheduling algorithms and digital tool for dynamic permutation flowshop with newly arrived order," International Journal of Production Research, Taylor & Francis Journals, vol. 55(11), pages 3234-3248, June.
    5. 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.
    6. H. Christopher Frey & Sumeet R. Patil, 2002. "Identification and Review of Sensitivity Analysis Methods," Risk Analysis, John Wiley & Sons, vol. 22(3), pages 553-578, June.
    7. Edris Yousefi Rad & Mohammad Reza Mahpeykar, 2017. "A Novel Hybrid Approach for Numerical Modeling of the Nucleating Flow in Laval Nozzle and Transonic Steam Turbine Blades," Energies, MDPI, vol. 10(9), pages 1-37, August.
    8. Tunali, S. & Batmaz, I., 2003. "A metamodeling methodology involving both qualitative and quantitative input factors," European Journal of Operational Research, Elsevier, vol. 150(2), pages 437-450, October.
    9. Jiang, Min & Huang, George Q., 2022. "Intralogistics synchronization in robotic forward-reserve warehouses for e-commerce last-mile delivery," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    10. Diaz, Rafael & Behr, Joshua G. & Acero, Beatriz, 2022. "Coastal housing recovery in a postdisaster environment: A supply chain perspective," International Journal of Production Economics, Elsevier, vol. 247(C).
    11. Kleijnen, J.P.C., 1995. "Sensitivity analysis and optimization of system dynamics models : Regression analysis and statistical design of experiments," Other publications TiSEM 87ee6ee0-592c-4204-ac50-6, Tilburg University, School of Economics and Management.
    12. Öner-Közen, Miray & Minner, Stefan, 2017. "Impact of priority sequencing decisions on on-time probability and expected tardiness of orders in make-to-order production systems with external due-dates," European Journal of Operational Research, Elsevier, vol. 263(2), pages 524-539.
    13. Sander van der Hoog, 2017. "Deep Learning in (and of) Agent-Based Models: A Prospectus," Papers 1706.06302, arXiv.org.
    14. Aumann, Craig A., 2007. "A methodology for developing simulation models of complex systems," Ecological Modelling, Elsevier, vol. 202(3), pages 385-396.
    15. Tanja Mlinar & Philippe Chevalier, 2016. "Pooling heterogeneous products for manufacturing environments," 4OR, Springer, vol. 14(2), pages 173-200, June.
    16. Giuliano Rancilio & Alexandre Lucas & Evangelos Kotsakis & Gianluca Fulli & Marco Merlo & Maurizio Delfanti & Marcelo Masera, 2019. "Modeling a Large-Scale Battery Energy Storage System for Power Grid Application Analysis," Energies, MDPI, vol. 12(17), pages 1-26, August.
    17. MacPherson, Brian & Gras, Robin, 2016. "Individual-based ecological models: Adjunctive tools or experimental systems?," Ecological Modelling, Elsevier, vol. 323(C), pages 106-114.
    18. Azaron, Amir & Katagiri, Hideki & Kato, Kosuke & Sakawa, Masatoshi, 2006. "Longest path analysis in networks of queues: Dynamic scheduling problems," European Journal of Operational Research, Elsevier, vol. 174(1), pages 132-149, October.
    19. Strang, Kenneth David, 2012. "Importance of verifying queue model assumptions before planning with simulation software," European Journal of Operational Research, Elsevier, vol. 218(2), pages 493-504.
    20. Branke, Juergen & Pickardt, Christoph W., 2011. "Evolutionary search for difficult problem instances to support the design of job shop dispatching rules," European Journal of Operational Research, Elsevier, vol. 212(1), pages 22-32, July.

    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:eee:proeco:v:144:y:2013:i:2:p:479-484. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    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.