IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v54y2016i22p6812-6824.html
   My bibliography  Save this article

Dynamic adjustment of dispatching rule parameters in flow shops with sequence-dependent set-up times

Author

Listed:
  • Jens Heger
  • Jürgen Branke
  • Torsten Hildebrandt
  • Bernd Scholz-Reiter

Abstract

Decentralised scheduling with dispatching rules is applied in many fields of production and logistics, especially in highly complex manufacturing systems. Since dispatching rules are restricted to their local information horizon, there is no rule that outperforms other rules across various objectives, scenarios and system conditions. In this paper, we present an approach to dynamically adjust the parameters of a dispatching rule depending on the current system conditions. The influence of different parameter settings of the chosen rule on the system performance is estimated by a machine learning method, whose learning data is generated by preliminary simulation runs. Using a dynamic flow shop scenario with sequence-dependent set-up times, we demonstrate that our approach is capable of significantly reducing the mean tardiness of jobs.

Suggested Citation

  • Jens Heger & Jürgen Branke & Torsten Hildebrandt & Bernd Scholz-Reiter, 2016. "Dynamic adjustment of dispatching rule parameters in flow shops with sequence-dependent set-up times," International Journal of Production Research, Taylor & Francis Journals, vol. 54(22), pages 6812-6824, November.
    • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:22:p:6812-6824
    DOI: 10.1080/00207543.2016.1178406
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2016.1178406
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207543.2016.1178406?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. Clyde L. Monma & Chris N. Potts, 1989. "On the Complexity of Scheduling with Batch Setup Times," Operations Research, INFORMS, vol. 37(5), pages 798-804, October.
    2. Lee, Young Hoon & Pinedo, Michael, 1997. "Scheduling jobs on parallel machines with sequence-dependent setup times," European Journal of Operational Research, Elsevier, vol. 100(3), pages 464-474, August.
    3. Joseph Adams & Egon Balas & Daniel Zawack, 1988. "The Shifting Bottleneck Procedure for Job Shop Scheduling," Management Science, INFORMS, vol. 34(3), pages 391-401, March.
    4. Jenny Chen & Michele Pfund & John Fowler & Douglas Montgomery & Thomas Callarman, 2010. "Robust scaling parameters for composite dispatching rules," IISE Transactions, Taylor & Francis Journals, vol. 42(11), pages 842-853.
    5. Aytug, Haldun & Lawley, Mark A. & McKay, Kenneth & Mohan, Shantha & Uzsoy, Reha, 2005. "Executing production schedules in the face of uncertainties: A review and some future directions," European Journal of Operational Research, Elsevier, vol. 161(1), pages 86-110, February.
    6. Rego, César & Duarte, Renato, 2009. "A filter-and-fan approach to the job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 194(3), pages 650-662, May.
    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. Xiuli Wu & Junjian Peng & Xiao Xiao & Shaomin Wu, 2021. "An effective approach for the dual-resource flexible job shop scheduling problem considering loading and unloading," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 707-728, March.
    2. Olumide Emmanuel Oluyisola & Swapnil Bhalla & Fabio Sgarbossa & Jan Ola Strandhagen, 2022. "Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 311-332, January.
    3. Juan Pablo Usuga Cadavid & Samir Lamouri & Bernard Grabot & Robert Pellerin & Arnaud Fortin, 2020. "Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1531-1558, August.
    4. Durk-Jouke van der Zee, 2017. "Coordinating batching decisions in manufacturing networks," International Journal of Production Research, Taylor & Francis Journals, vol. 55(18), pages 5405-5422, September.
    5. Toorajipour, Reza & Sohrabpour, Vahid & Nazarpour, Ali & Oghazi, Pejvak & Fischl, Maria, 2021. "Artificial intelligence in supply chain management: A systematic literature review," Journal of Business Research, Elsevier, vol. 122(C), pages 502-517.
    6. Andreas Kuhnle & Jan-Philipp Kaiser & Felix Theiß & Nicole Stricker & Gisela Lanza, 2021. "Designing an adaptive production control system using reinforcement learning," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 855-876, March.

    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. Joao António Noivo & Helena Ramalhinho-Lourenço, 1998. "Solving two production scheduling problems with sequence-dependent set-up times," Economics Working Papers 338, Department of Economics and Business, Universitat Pompeu Fabra.
    2. Dauzère-Pérès, Stéphane & Ding, Junwen & Shen, Liji & Tamssaouet, Karim, 2024. "The flexible job shop scheduling problem: A review," European Journal of Operational Research, Elsevier, vol. 314(2), pages 409-432.
    3. Shahed Mahmud & Ripon K. Chakrabortty & Alireza Abbasi & Michael J. Ryan, 2022. "Switching strategy-based hybrid evolutionary algorithms for job shop scheduling problems," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 1939-1966, October.
    4. Altekin, F. Tevhide & Bukchin, Yossi, 2022. "A multi-objective optimization approach for exploring the cost and makespan trade-off in additive manufacturing," European Journal of Operational Research, Elsevier, vol. 301(1), pages 235-253.
    5. C N Potts & V A Strusevich, 2009. "Fifty years of scheduling: a survey of milestones," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 41-68, May.
    6. Selcuk Goren & Ihsan Sabuncuoglu & Utku Koc, 2012. "Optimization of schedule stability and efficiency under processing time variability and random machine breakdowns in a job shop environment," Naval Research Logistics (NRL), John Wiley & Sons, vol. 59(1), pages 26-38, February.
    7. 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).
    8. Pickardt, Christoph W. & Hildebrandt, Torsten & Branke, Jürgen & Heger, Jens & Scholz-Reiter, Bernd, 2013. "Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems," International Journal of Production Economics, Elsevier, vol. 145(1), pages 67-77.
    9. César Rego & Fred Glover, 2010. "Ejection chain and filter-and-fan methods in combinatorial optimization," Annals of Operations Research, Springer, vol. 175(1), pages 77-105, March.
    10. Huiqiao Su & Michael Pinedo & Guohua Wan, 2017. "Parallel machine scheduling with eligibility constraints: A composite dispatching rule to minimize total weighted tardiness," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(3), pages 249-267, April.
    11. Gromicho, J.A.S. & Hoorn, J.J. van & Timmer, G.T., 2009. "Exponentially better than brute force: solving the jobshop scheduling problem optimally by dynamic programming," Serie Research Memoranda 0056, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    12. 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.
    13. Alexey Matveev & Varvara Feoktistova & Ksenia Bolshakova, 2016. "On Global Near Optimality of Special Periodic Protocols for Fluid Polling Systems with Setups," Journal of Optimization Theory and Applications, Springer, vol. 171(3), pages 1055-1070, December.
    14. Sels, Veronique & Craeymeersch, Kjeld & Vanhoucke, Mario, 2011. "A hybrid single and dual population search procedure for the job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 215(3), pages 512-523, December.
    15. 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.
    16. Alidaee, Bahram & Kochenberger, Gary A. & Amini, Mohammad M., 2001. "Greedy solutions of selection and ordering problems," European Journal of Operational Research, Elsevier, vol. 134(1), pages 203-215, October.
    17. Ganesan, Viswanath Kumar & Sivakumar, Appa Iyer, 2006. "Scheduling in static jobshops for minimizing mean flowtime subject to minimum total deviation of job completion times," International Journal of Production Economics, Elsevier, vol. 103(2), pages 633-647, October.
    18. Faicel Hnaien & Taha Arbaoui, 2023. "Minimizing the makespan for the two-machine flow shop scheduling problem with random breakdown," Annals of Operations Research, Springer, vol. 328(2), pages 1437-1460, September.
    19. Fowler, John W. & Mönch, Lars, 2022. "A survey of scheduling with parallel batch (p-batch) processing," European Journal of Operational Research, Elsevier, vol. 298(1), pages 1-24.
    20. Ramalhinho Lourenco, Helena, 1996. "Sevast'yanov's algorithm for the flow-shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 91(1), pages 176-189, May.

    More about this item

    Statistics

    Access and download statistics

    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:taf:tprsxx:v:54:y:2016:i:22:p:6812-6824. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TPRS20 .

    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.