IDEAS home Printed from https://ideas.repec.org/a/spr/operea/v18y2018i1d10.1007_s12351-016-0257-6.html
   My bibliography  Save this article

An efficient bi-objective algorithm to solve re-entrant hybrid flow shop scheduling with learning effect and setup times

Author

Listed:
  • S. M. Mousavi

    (Mazandaran University of Science and Technology)

  • I. Mahdavi

    (Mazandaran University of Science and Technology)

  • J. Rezaeian

    (Mazandaran University of Science and Technology)

  • M. Zandieh

    (Shahid Beheshti University, G. C.)

Abstract

This paper deals with a bi-objective hybrid flow shop scheduling problem minimizing the maximum completion time (makespan) and total tardiness, in which we consider re-entrant lines, setup times and position-dependent learning effects. The solution method based on genetic algorithm is proposed to solve the problem approximately, which belongs to non-deterministic polynomial-time (NP)-hard class. The solution procedure is categorized through methods where various solutions are found and then, the decision-makers select the most adequate (a posteriori approach). Taguchi method is applied to set the parameters of proposed algorithm. To demonstrate the validation of proposed algorithm, the full enumeration algorithm is used to find the Pareto-optimal front for special small problems. To show the efficiency and effectiveness of the proposed algorithm in comparison with other efficient algorithm in the literature (namely MLPGA) on our problem, the experiments were conducted on three dimensions of problems: small, medium and large. Computational results are expressed in terms of standard multi-objective metrics. The results show that the proposed algorithm is able to obtain more diversified and competitive Pareto sets than the MLPGA.

Suggested Citation

  • S. M. Mousavi & I. Mahdavi & J. Rezaeian & M. Zandieh, 2018. "An efficient bi-objective algorithm to solve re-entrant hybrid flow shop scheduling with learning effect and setup times," Operational Research, Springer, vol. 18(1), pages 123-158, April.
    • Handle: RePEc:spr:operea:v:18:y:2018:i:1:d:10.1007_s12351-016-0257-6
    DOI: 10.1007/s12351-016-0257-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12351-016-0257-6
    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/s12351-016-0257-6?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. Ruiz, Rubén & Vázquez-Rodríguez, José Antonio, 2010. "The hybrid flow shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 205(1), pages 1-18, August.
    2. Allahverdi, Ali & Gupta, Jatinder N. D. & Aldowaisan, Tariq, 1999. "A review of scheduling research involving setup considerations," Omega, Elsevier, vol. 27(2), pages 219-239, April.
    3. Biskup, Dirk, 1999. "Single-machine scheduling with learning considerations," European Journal of Operational Research, Elsevier, vol. 115(1), pages 173-178, May.
    4. Dugardin, Frédéric & Yalaoui, Farouk & Amodeo, Lionel, 2010. "New multi-objective method to solve reentrant hybrid flow shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 203(1), pages 22-31, May.
    5. Allahverdi, Ali & Ng, C.T. & Cheng, T.C.E. & Kovalyov, Mikhail Y., 2008. "A survey of scheduling problems with setup times or costs," European Journal of Operational Research, Elsevier, vol. 187(3), pages 985-1032, June.
    6. Biskup, Dirk, 2008. "A state-of-the-art review on scheduling with learning effects," European Journal of Operational Research, Elsevier, vol. 188(2), pages 315-329, July.
    7. M. Y. Wang & S. P. Sethi & S. L. van de Velde, 1997. "Minimizing Makespan in a Class of Reentrant Shops," Operations Research, INFORMS, vol. 45(5), pages 702-712, October.
    8. Andres, Carlos & Albarracin, Jose Miguel & Tormo, Guillermina & Vicens, Eduardo & Garcia-Sabater, Jose Pedro, 2005. "Group technology in a hybrid flowshop environment: A case study," European Journal of Operational Research, Elsevier, vol. 167(1), pages 272-281, November.
    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. Julio Mar-Ortiz & Alex J. Ruiz Torres & Belarmino Adenso-Díaz, 2022. "Scheduling in parallel machines with two objectives: analysis of factors that influence the Pareto frontier," Operational Research, Springer, vol. 22(4), pages 4585-4605, September.
    2. Chen Peng & Tao Peng & Yi Zhang & Renzhong Tang & Luoke Hu, 2018. "Minimising Non-Processing Energy Consumption and Tardiness Fines in a Mixed-Flow Shop," Energies, MDPI, vol. 11(12), pages 1-15, December.
    3. Maedeh Fasihi & Reza Tavakkoli-Moghaddam & Fariborz Jolai, 2023. "A bi-objective re-entrant permutation flow shop scheduling problem: minimizing the makespan and maximum tardiness," Operational Research, Springer, vol. 23(2), pages 1-41, June.
    4. Hongtao Tang & Jiahao Zhou & Yiping Shao & Zhixiong Yang, 2023. "Hybrid Flow-Shop Scheduling Problems with Missing and Re-Entrant Operations Considering Process Scheduling and Production of Energy Consumption," Sustainability, MDPI, vol. 15(10), pages 1-19, May.
    5. Neufeld, Janis S. & Schulz, Sven & Buscher, Udo, 2023. "A systematic review of multi-objective hybrid flow shop scheduling," European Journal of Operational Research, Elsevier, vol. 309(1), pages 1-23.

    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. Zhang, Zhe & Song, Xiaoling & Huang, Huijung & Zhou, Xiaoyang & Yin, Yong, 2022. "Logic-based Benders decomposition method for the seru scheduling problem with sequence-dependent setup time and DeJong’s learning effect," European Journal of Operational Research, Elsevier, vol. 297(3), pages 866-877.
    2. Bozorgirad, Mir Abbas & Logendran, Rasaratnam, 2013. "Bi-criteria group scheduling in hybrid flowshops," International Journal of Production Economics, Elsevier, vol. 145(2), pages 599-612.
    3. Fátima Pilar & Eliana Costa e Silva & Ana Borges, 2023. "Optimizing Vehicle Repairs Scheduling Using Mixed Integer Linear Programming: A Case Study in the Portuguese Automobile Sector," Mathematics, MDPI, vol. 11(11), pages 1-23, June.
    4. Zhang Xingong & Wang Yong & Bai Shikun, 2016. "Single-machine group scheduling problems with deteriorating and learning effect," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(10), pages 2402-2410, July.
    5. Missaoui, Ahmed & Ruiz, Rubén, 2022. "A parameter-Less iterated greedy method for the hybrid flowshop scheduling problem with setup times and due date windows," European Journal of Operational Research, Elsevier, vol. 303(1), pages 99-113.
    6. Rossit, Daniel Alejandro & Tohmé, Fernando & Frutos, Mariano, 2018. "The Non-Permutation Flow-Shop scheduling problem: A literature review," Omega, Elsevier, vol. 77(C), pages 143-153.
    7. Wang, Kai & Qin, Hu & Huang, Yun & Luo, Mengwen & Zhou, Lei, 2021. "Surgery scheduling in outpatient procedure centre with re-entrant patient flow and fuzzy service times," Omega, Elsevier, vol. 102(C).
    8. Xingong Zhang & Guangle Yan & Wanzhen Huang & Guochun Tang, 2011. "Single-machine scheduling problems with time and position dependent processing times," Annals of Operations Research, Springer, vol. 186(1), pages 345-356, June.
    9. Marko Ɖurasević & Domagoj Jakobović, 2019. "Creating dispatching rules by simple ensemble combination," Journal of Heuristics, Springer, vol. 25(6), pages 959-1013, December.
    10. Wen-Hung Wu & Yunqiang Yin & T C E Cheng & Win-Chin Lin & Juei-Chao Chen & Shin-Yi Luo & Chin-Chia Wu, 2017. "A combined approach for two-agent scheduling with sum-of-processing-times-based learning effect," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(2), pages 111-120, February.
    11. Shahvari, Omid & Logendran, Rasaratnam, 2016. "Hybrid flow shop batching and scheduling with a bi-criteria objective," International Journal of Production Economics, Elsevier, vol. 179(C), pages 239-258.
    12. Og[breve]uz, Ceyda & Sibel Salman, F. & Bilgintürk YalçIn, Zehra, 2010. "Order acceptance and scheduling decisions in make-to-order systems," International Journal of Production Economics, Elsevier, vol. 125(1), pages 200-211, May.
    13. Dirk Briskorn & Konrad Stephan & Nils Boysen, 2022. "Minimizing the makespan on a single machine subject to modular setups," Journal of Scheduling, Springer, vol. 25(1), pages 125-137, February.
    14. Jin Xu & Natarajan Gautam, 2020. "On competitive analysis for polling systems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(6), pages 404-419, September.
    15. Hongyu He & Yanzhi Zhao & Xiaojun Ma & Yuan-Yuan Lu & Na Ren & Ji-Bo Wang, 2023. "Study on Scheduling Problems with Learning Effects and Past Sequence Delivery Times," Mathematics, MDPI, vol. 11(19), pages 1-19, September.
    16. Grundel, Soesja & Çiftçi, Barış & Borm, Peter & Hamers, Herbert, 2013. "Family sequencing and cooperation," European Journal of Operational Research, Elsevier, vol. 226(3), pages 414-424.
    17. Mohammad Reza Hosseinzadeh & Mehdi Heydari & Mohammad Mahdavi Mazdeh, 2022. "Mathematical modeling and two metaheuristic algorithms for integrated process planning and group scheduling with sequence-dependent setup time," Operational Research, Springer, vol. 22(5), pages 5055-5105, November.
    18. Sheikh, Shaya & Komaki, G.M. & Kayvanfar, Vahid & Teymourian, Ehsan, 2019. "Multi-Stage assembly flow shop with setup time and release time," Operations Research Perspectives, Elsevier, vol. 6(C).
    19. Asghari, M. & Afshari, H. & Jaber, M.Y. & Searcy, C., 2024. "Learning and forgetting interactions within a collaborative human-centric manufacturing network," European Journal of Operational Research, Elsevier, vol. 313(3), pages 977-991.
    20. Cheng, T.C.E. & Shafransky, Y. & Ng, C.T., 2016. "An alternative approach for proving the NP-hardness of optimization problems," European Journal of Operational Research, Elsevier, vol. 248(1), pages 52-58.

    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:operea:v:18:y:2018:i:1:d:10.1007_s12351-016-0257-6. 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.