IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v33y2022i6d10.1007_s10845-020-01713-8.html
   My bibliography  Save this article

Research on flexible job-shop scheduling problem in green sustainable manufacturing based on learning effect

Author

Listed:
  • Zhao Peng

    (Wuhan University of Technology)

  • Huan Zhang

    (Wuhan University of Technology)

  • Hongtao Tang

    (Wuhan University of Technology)

  • Yue Feng

    (Wuhan University of Technology)

  • Weiming Yin

    (Wuhan University of Technology)

Abstract

As one of the manufacturing industries with high energy consumption and high pollution, sand casting is facing major challenges in green manufacturing. In order to balance production and green sustainable development, this paper puts forward man–machine dual resource constraint mechanism. In addition, a multi-objective flexible job shop scheduling problem model constrained by job transportation time and learning effect is constructed, and the goal is to minimize processing time energy consumption and noise. Subsequently, a hybrid discrete multi-objective imperial competition algorithm (HDMICA) is developed to solve the model. The global search mechanism based on the HDMICA improves two aspects: a new initialization method to improve the quality of the initial population, and the empire selection method based on Pareto non-dominated solution to balance the empire forces. Then, the improved simulated annealing algorithm is embedded in imperial competition algorithm (ICA), which overcomes the premature convergence problem of ICA. Therefore, four neighborhood structures are designed to help the algorithm jump out of the local optimal solution. Finally, an example is used to verify the feasibility of the proposed algorithm. By comparing with the original ICA and other four algorithms, the effectiveness of the proposed algorithm in the quality of the first frontier solution is verified.

Suggested Citation

  • Zhao Peng & Huan Zhang & Hongtao Tang & Yue Feng & Weiming Yin, 2022. "Research on flexible job-shop scheduling problem in green sustainable manufacturing based on learning effect," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1725-1746, August.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:6:d:10.1007_s10845-020-01713-8
    DOI: 10.1007/s10845-020-01713-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-020-01713-8
    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/s10845-020-01713-8?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. Boutsinas, Basilis, 2013. "Machine-part cell formation using biclustering," European Journal of Operational Research, Elsevier, vol. 230(3), pages 563-572.
    2. Jian Zhang & Guofu Ding & Yisheng Zou & Shengfeng Qin & Jianlin Fu, 2019. "Review of job shop scheduling research and its new perspectives under Industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1809-1830, April.
    3. Mingtao Wu & Zhengyi Song & Young B. Moon, 2019. "Detecting cyber-physical attacks in CyberManufacturing systems with machine learning methods," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1111-1123, March.
    4. Eugeniusz Nowicki & Czeslaw Smutnicki, 1996. "A Fast Taboo Search Algorithm for the Job Shop Problem," Management Science, INFORMS, vol. 42(6), pages 797-813, June.
    5. Biskup, Dirk, 1999. "Single-machine scheduling with learning considerations," European Journal of Operational Research, Elsevier, vol. 115(1), pages 173-178, May.
    6. Seyedmohsen Hosseini & Abdullah Al Khaled, 2019. "A hybrid ensemble and AHP approach for resilient supplier selection," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 207-228, January.
    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. Gregory A. Kasapidis & Dimitris C. Paraskevopoulos & Panagiotis P. Repoussis & Christos D. Tarantilis, 2021. "Flexible Job Shop Scheduling Problems with Arbitrary Precedence Graphs," Production and Operations Management, Production and Operations Management Society, vol. 30(11), pages 4044-4068, November.
    2. Raja Awais Liaqait & Shermeen Hamid & Salman Sagheer Warsi & Azfar Khalid, 2021. "A Critical Analysis of Job Shop Scheduling in Context of Industry 4.0," Sustainability, MDPI, vol. 13(14), pages 1-19, July.
    3. 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.
    4. Shen, Liji & Buscher, Udo, 2012. "Solving the serial batching problem in job shop manufacturing systems," European Journal of Operational Research, Elsevier, vol. 221(1), pages 14-26.
    5. Derya Deliktaş, 2022. "Self-adaptive memetic algorithms for multi-objective single machine learning-effect scheduling problems with release times," Flexible Services and Manufacturing Journal, Springer, vol. 34(3), pages 748-784, September.
    6. Ghanei, Shima & Contreras, Ivan & Cordeau, Jean-François, 2023. "A two-stage stochastic collaborative intertwined supply network design problem under multiple disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 170(C).
    7. 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.
    8. Jiae Zhang & Jianjun Yang, 2016. "Flexible job-shop scheduling with flexible workdays, preemption, overlapping in operations and satisfaction criteria: an industrial application," International Journal of Production Research, Taylor & Francis Journals, vol. 54(16), pages 4894-4918, August.
    9. García-Villoria, Alberto & Corominas, Albert & Nadal, Adrià & Pastor, Rafael, 2018. "Solving the accessibility windows assembly line problem level 1 and variant 1 (AWALBP-L1-1) with precedence constraints," European Journal of Operational Research, Elsevier, vol. 271(3), pages 882-895.
    10. Antonio Zavala-Alcívar & María-José Verdecho & Juan-José Alfaro-Saiz, 2020. "A Conceptual Framework to Manage Resilience and Increase Sustainability in the Supply Chain," Sustainability, MDPI, vol. 12(16), pages 1-38, August.
    11. Pempera, Jaroslaw & Smutnicki, Czeslaw, 2018. "Open shop cyclic scheduling," European Journal of Operational Research, Elsevier, vol. 269(2), pages 773-781.
    12. Chen, Ziyue & Huang, Lizhen, 2021. "Digital twins for information-sharing in remanufacturing supply chain: A review," Energy, Elsevier, vol. 220(C).
    13. Xiaobao Zhu & Jing Shi & Fengjie Xie & Rouqi Song, 2020. "Pricing strategy and system performance in a cloud-based manufacturing system built on blockchain technology," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1985-2002, December.
    14. Liaw, Ching-Fang, 2000. "A hybrid genetic algorithm for the open shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 124(1), pages 28-42, July.
    15. Masoud Zafarzadeh & Magnus Wiktorsson & Jannicke Baalsrud Hauge, 2021. "A Systematic Review on Technologies for Data-Driven Production Logistics: Their Role from a Holistic and Value Creation Perspective," Logistics, MDPI, vol. 5(2), pages 1-32, April.
    16. Hamta, Nima & Fatemi Ghomi, S.M.T. & Jolai, F. & Akbarpour Shirazi, M., 2013. "A hybrid PSO algorithm for a multi-objective assembly line balancing problem with flexible operation times, sequence-dependent setup times and learning effect," International Journal of Production Economics, Elsevier, vol. 141(1), pages 99-111.
    17. 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.
    18. Zigao Wu & Shaohua Yu & Tiancheng Li, 2019. "A Meta-Model-Based Multi-Objective Evolutionary Approach to Robust Job Shop Scheduling," Mathematics, MDPI, vol. 7(6), pages 1-19, June.
    19. Bürgy, Reinhard & Bülbül, Kerem, 2018. "The job shop scheduling problem with convex costs," European Journal of Operational Research, Elsevier, vol. 268(1), pages 82-100.
    20. 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.

    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:joinma:v:33:y:2022:i:6:d:10.1007_s10845-020-01713-8. 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.