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. 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.
    2. 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.
    3. Biskup, Dirk, 1999. "Single-machine scheduling with learning considerations," European Journal of Operational Research, Elsevier, vol. 115(1), pages 173-178, May.
    4. Boutsinas, Basilis, 2013. "Machine-part cell formation using biclustering," European Journal of Operational Research, Elsevier, vol. 230(3), pages 563-572.
    5. 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.
    6. 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.
    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. Lixiang Zhang & Yan Yan & Yaoguang Hu, 2024. "Deep reinforcement learning for dynamic scheduling of energy-efficient automated guided vehicles," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 3875-3888, December.

    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. Fatih Yiğit & Şakir Esnaf, 2021. "A new Fuzzy C-Means and AHP-based three-phased approach for multiple criteria ABC inventory classification," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1517-1528, August.
    5. Groflin, Heinz & Klinkert, Andreas, 2007. "Feasible insertions in job shop scheduling, short cycles and stable sets," European Journal of Operational Research, Elsevier, vol. 177(2), pages 763-785, March.
    6. Zoltán Varga & Pál Simon, 2014. "Examination Of Scheduling Methods For Production Systems," Advanced Logistic systems, University of Miskolc, Department of Material Handling and Logistics, vol. 8(1), pages 111-120, December.
    7. Tao Ren & Yan Zhang & Shuenn-Ren Cheng & Chin-Chia Wu & Meng Zhang & Bo-yu Chang & Xin-yue Wang & Peng Zhao, 2020. "Effective Heuristic Algorithms Solving the Jobshop Scheduling Problem with Release Dates," Mathematics, MDPI, vol. 8(8), pages 1-25, July.
    8. 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.
    9. Rossi, Andrea, 2014. "Flexible job shop scheduling with sequence-dependent setup and transportation times by ant colony with reinforced pheromone relationships," International Journal of Production Economics, Elsevier, vol. 153(C), pages 253-267.
    10. 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.
    11. Bai, Danyu & Tang, Mengqian & Zhang, Zhi-Hai & Santibanez-Gonzalez, Ernesto DR, 2018. "Flow shop learning effect scheduling problem with release dates," Omega, Elsevier, vol. 78(C), pages 21-38.
    12. Battaïa, Olga & Dolgui, Alexandre, 2013. "A taxonomy of line balancing problems and their solutionapproaches," International Journal of Production Economics, Elsevier, vol. 142(2), pages 259-277.
    13. 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).
    14. 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.
    15. 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.
    16. Min Ji & Chou-Jung Hsu & Dar-Li Yang, 2013. "Single-machine scheduling with deteriorating jobs and aging effects under an optional maintenance activity consideration," Journal of Combinatorial Optimization, Springer, vol. 26(3), pages 437-447, October.
    17. Meloni, Carlo & Pranzo, Marco & Samà, Marcella, 2022. "Evaluation of VaR and CVaR for the makespan in interval valued blocking job shops," International Journal of Production Economics, Elsevier, vol. 247(C).
    18. Buscher, Udo & Shen, Liji, 2009. "An integrated tabu search algorithm for the lot streaming problem in job shops," European Journal of Operational Research, Elsevier, vol. 199(2), pages 385-399, December.
    19. 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.
    20. Fangzhong Qi & Leilei Zhang & Kexiang Zhuo & Xiuyan Ma, 2022. "Early Warning for Manufacturing Supply Chain Resilience Based on Improved Grey Prediction Model," Sustainability, MDPI, vol. 14(20), pages 1-16, October.

    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.