IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v611y2023ics0378437123000122.html
   My bibliography  Save this article

An uncertain permutation flow shop predictive scheduling problem with processing interruption

Author

Listed:
  • Shen, Jiayu
  • Shi, Yuanji
  • Shi, Jianxin
  • Dai, Yunzhong
  • Li, Wei

Abstract

In this study, a permutation flow shop scheduling problem is examined. Due to a large number of uncertain factors in reality, the machine may be interrupted by many events during the processing. At this time, if the implementation is still carried out according to the original plan, it may deviate from the desired result. Therefore, the sudden machine failure is considered. The objective function is to find the pessimistic value of makespan. To explore the influence of uncertainty on decision variables and avoid frequent use of rescheduling strategy, a chance constrained programming model with faults is established. In accordance with the uncertainty theory, we derive the deterministic equivalence of the proposed model. A hybrid genetic algorithm combined with asynchronous evolution is proposed to solve this model. Additionally, the model is analyzed and special properties are proposed. Finally, the effectiveness of the modeling method is verified by numerical experiments. Moreover, it also shows that the hybrid genetic algorithm has greater advantages than the rescheduling strategy.

Suggested Citation

  • Shen, Jiayu & Shi, Yuanji & Shi, Jianxin & Dai, Yunzhong & Li, Wei, 2023. "An uncertain permutation flow shop predictive scheduling problem with processing interruption," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).
    • Handle: RePEc:eee:phsmap:v:611:y:2023:i:c:s0378437123000122
    DOI: 10.1016/j.physa.2023.128457
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437123000122
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2023.128457?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. Levorato, Mario & Figueiredo, Rosa & Frota, Yuri, 2022. "Exact solutions for the two-machine robust flow shop with budgeted uncertainty," European Journal of Operational Research, Elsevier, vol. 300(1), pages 46-57.
    2. Yu, Tae-Sun & Han, Jun-Hee, 2021. "Scheduling proportionate flow shops with preventive machine maintenance," International Journal of Production Economics, Elsevier, vol. 231(C).
    3. Ruiz, Rubén & Maroto, Concepciøn & Alcaraz, Javier, 2006. "Two new robust genetic algorithms for the flowshop scheduling problem," Omega, Elsevier, vol. 34(5), pages 461-476, October.
    4. Alcaide, D. & Rodriguez-Gonzalez, A. & Sicilia, J., 2002. "An approach to solve the minimum expected makespan flow-shop problem subject to breakdowns," European Journal of Operational Research, Elsevier, vol. 140(2), pages 384-398, July.
    5. Nawaz, Muhammad & Enscore Jr, E Emory & Ham, Inyong, 1983. "A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem," Omega, Elsevier, vol. 11(1), pages 91-95.
    6. E. Savku & G.-W Weber, 2022. "Stochastic differential games for optimal investment problems in a Markov regime-switching jump-diffusion market," Annals of Operations Research, Springer, vol. 312(2), pages 1171-1196, May.
    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. Ruiz, Ruben & Stutzle, Thomas, 2007. "A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem," European Journal of Operational Research, Elsevier, vol. 177(3), pages 2033-2049, March.
    2. Fernandez-Viagas, Victor & Ruiz, Rubén & Framinan, Jose M., 2017. "A new vision of approximate methods for the permutation flowshop to minimise makespan: State-of-the-art and computational evaluation," European Journal of Operational Research, Elsevier, vol. 257(3), pages 707-721.
    3. Kalczynski, Pawel J. & Kamburowski, Jerzy, 2009. "An empirical analysis of the optimality rate of flow shop heuristics," European Journal of Operational Research, Elsevier, vol. 198(1), pages 93-101, October.
    4. Sachchida Nand Chaurasia & Shyam Sundar & Alok Singh, 2017. "Hybrid metaheuristic approaches for the single machine total stepwise tardiness problem with release dates," Operational Research, Springer, vol. 17(1), pages 275-295, April.
    5. Wahiba Jomaa & Mansour Eddaly & Bassem Jarboui, 2021. "Variable neighborhood search algorithms for the permutation flowshop scheduling problem with the preventive maintenance," Operational Research, Springer, vol. 21(4), pages 2525-2542, December.
    6. Pan, Quan-Ke & Ruiz, Rubén, 2014. "An effective iterated greedy algorithm for the mixed no-idle permutation flowshop scheduling problem," Omega, Elsevier, vol. 44(C), pages 41-50.
    7. Xiong, Fuli & Xing, Keyi & Wang, Feng, 2015. "Scheduling a hybrid assembly-differentiation flowshop to minimize total flow time," European Journal of Operational Research, Elsevier, vol. 240(2), pages 338-354.
    8. Angel A. Juan & Helena Ramalhinho-Lourenço & Manuel Mateo & Quim Castellà & Barry B. Barrios, 2012. "ILS-ESP: An efficient, simple, and parameter-free algorithm for solving the permutation flow-shop problem," Economics Working Papers 1319, Department of Economics and Business, Universitat Pompeu Fabra.
    9. Pan, Quan-Ke & Ruiz, Rubén, 2012. "An estimation of distribution algorithm for lot-streaming flow shop problems with setup times," Omega, Elsevier, vol. 40(2), pages 166-180, April.
    10. Pan, Quan-Ke & Wang, Ling, 2012. "Effective heuristics for the blocking flowshop scheduling problem with makespan minimization," Omega, Elsevier, vol. 40(2), pages 218-229, April.
    11. Benavides, Alexander J. & Ritt, Marcus & Miralles, Cristóbal, 2014. "Flow shop scheduling with heterogeneous workers," European Journal of Operational Research, Elsevier, vol. 237(2), pages 713-720.
    12. Martín Ravetti & Carlos Riveros & Alexandre Mendes & Mauricio Resende & Panos Pardalos, 2012. "Parallel hybrid heuristics for the permutation flow shop problem," Annals of Operations Research, Springer, vol. 199(1), pages 269-284, October.
    13. Vallada, Eva & Ruiz, Rubén & Framinan, Jose M., 2015. "New hard benchmark for flowshop scheduling problems minimising makespan," European Journal of Operational Research, Elsevier, vol. 240(3), pages 666-677.
    14. Vallada, Eva & Ruiz, Rubén, 2009. "Cooperative metaheuristics for the permutation flowshop scheduling problem," European Journal of Operational Research, Elsevier, vol. 193(2), pages 365-376, March.
    15. Naderi, Bahman & Ruiz, Rubén, 2014. "A scatter search algorithm for the distributed permutation flowshop scheduling problem," European Journal of Operational Research, Elsevier, vol. 239(2), pages 323-334.
    16. Yenisey, Mehmet Mutlu & Yagmahan, Betul, 2014. "Multi-objective permutation flow shop scheduling problem: Literature review, classification and current trends," Omega, Elsevier, vol. 45(C), pages 119-135.
    17. Zhang, Yi & Li, Xiaoping & Wang, Qian, 2009. "Hybrid genetic algorithm for permutation flowshop scheduling problems with total flowtime minimization," European Journal of Operational Research, Elsevier, vol. 196(3), pages 869-876, August.
    18. Barry B. & Quim Castellà & Angel A. & Helena Ramalhinho Lourenco & Manuel Mateo, 2012. "ILS-ESP: An Efficient, Simple, and Parameter-Free Algorithm for Solving the Permutation Flow-Shop Problem," Working Papers 636, Barcelona School of Economics.
    19. Pessoa, Luciana S. & Andrade, Carlos E., 2018. "Heuristics for a flowshop scheduling problem with stepwise job objective function," European Journal of Operational Research, Elsevier, vol. 266(3), pages 950-962.
    20. Chang, Pei-Chann & Huang, Wei-Hsiu & Wu, Jheng-Long & Cheng, T.C.E., 2013. "A block mining and re-combination enhanced genetic algorithm for the permutation flowshop scheduling problem," International Journal of Production Economics, Elsevier, vol. 141(1), pages 45-55.

    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:phsmap:v:611:y:2023:i:c:s0378437123000122. 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.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.