IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i19p12120-d924739.html
   My bibliography  Save this article

An Improved Mayfly Method to Solve Distributed Flexible Job Shop Scheduling Problem under Dual Resource Constraints

Author

Listed:
  • Shoujing Zhang

    (Department of Industrial Engineering, Xi’an Key Laboratory of Modern Intelligent Textile Equipment, Xi’an Polytechnic University, Xi’an 710600, China)

  • Tiantian Hou

    (Department of Industrial Engineering, Xi’an Key Laboratory of Modern Intelligent Textile Equipment, Xi’an Polytechnic University, Xi’an 710600, China)

  • Qing Qu

    (Department of Industrial Engineering, Xi’an Key Laboratory of Modern Intelligent Textile Equipment, Xi’an Polytechnic University, Xi’an 710600, China)

  • Adam Glowacz

    (Department of Automatic, Control and Robotics, AGH University of Science and Technology, 30-059 Kraków, Poland)

  • Samar M. Alqhtani

    (Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia)

  • Muhammad Irfan

    (Electrical Engineering Department, College of Engineering, Najran University Saudi Arabia, Najran 61441, Saudi Arabia)

  • Grzegorz Królczyk

    (Faculty of Mechanical Engineering, Opole University of Technology, 45-758 Opole, Poland)

  • Zhixiong Li

    (Faculty of Mechanical Engineering, Opole University of Technology, 45-758 Opole, Poland
    Yonsei Frontier Lab, Yonsei University, Seoul 03722, Korea)

Abstract

Aiming at the distributed flexible job shop scheduling problem under dual resource constraints considering the influence of workpiece transportation time between factories and machines, a distributed flexible job shop scheduling problem (DFJSP) model with the optimization goal of minimizing completion time is established, and an improved mayfly algorithm (IMA) is proposed to solve it. Firstly, the mayfly position vector is discrete mapped to make it applicable to the scheduling problem. Secondly, three-layer coding rules of process, worker, and machine is adopted, in which the factory selection is reflected by machine number according to the characteristics of the model, and a hybrid initialization strategy is designed to improve the population quality and diversity. Thirdly, an active time window decoding strategy considering transportation time is designed for the worker–machine idle time window to improve the local optimization performance of the algorithm. In addition, the improved crossover and mutation operators is designed to expand the global search range of the algorithm. Finally, through simulation experiments, the results of various algorithms are compared to verify the effectiveness of the proposed algorithm for isomorphism and isomerism factories instances.

Suggested Citation

  • Shoujing Zhang & Tiantian Hou & Qing Qu & Adam Glowacz & Samar M. Alqhtani & Muhammad Irfan & Grzegorz Królczyk & Zhixiong Li, 2022. "An Improved Mayfly Method to Solve Distributed Flexible Job Shop Scheduling Problem under Dual Resource Constraints," Sustainability, MDPI, vol. 14(19), pages 1-19, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12120-:d:924739
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/19/12120/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/19/12120/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Guiliang Gong & Raymond Chiong & Qianwang Deng & Xuran Gong, 2020. "A hybrid artificial bee colony algorithm for flexible job shop scheduling with worker flexibility," International Journal of Production Research, Taylor & Francis Journals, vol. 58(14), pages 4406-4420, July.
    2. De Giovanni, L. & Pezzella, F., 2010. "An Improved Genetic Algorithm for the Distributed and Flexible Job-shop Scheduling problem," European Journal of Operational Research, Elsevier, vol. 200(2), pages 395-408, January.
    3. Xiao-long Zheng & Ling Wang, 2016. "A knowledge-guided fruit fly optimization algorithm for dual resource constrained flexible job-shop scheduling problem," International Journal of Production Research, Taylor & Francis Journals, vol. 54(18), pages 5554-5566, September.
    4. Guiliang Gong & Raymond Chiong & Qianwang Deng & Qiang Luo, 2020. "A memetic algorithm for multi-objective distributed production scheduling: minimizing the makespan and total energy consumption," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1443-1466, August.
    5. Liping Zhou & Zhibin Jiang & Na Geng & Yimeng Niu & Feng Cui & Kefei Liu & Nanshan Qi, 2022. "Production and operations management for intelligent manufacturing: a systematic literature review," International Journal of Production Research, Taylor & Francis Journals, vol. 60(2), pages 808-846, January.
    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. Bin Ji & Shujing Zhang & Samson S. Yu & Binqiao Zhang, 2023. "Mathematical Modeling and A Novel Heuristic Method for Flexible Job-Shop Batch Scheduling Problem with Incompatible Jobs," Sustainability, MDPI, vol. 15(3), pages 1-26, January.

    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. Aidin Delgoshaei & Mohd Khairol Anuar Bin Mohd Ariffin & Zulkiflle B. Leman, 2022. "An Effective 4–Phased Framework for Scheduling Job-Shop Manufacturing Systems Using Weighted NSGA-II," Mathematics, MDPI, vol. 10(23), pages 1-28, December.
    2. 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.
    3. Jose L. Andrade-Pineda & David Canca & Pedro L. Gonzalez-R & M. Calle, 2020. "Scheduling a dual-resource flexible job shop with makespan and due date-related criteria," Annals of Operations Research, Springer, vol. 291(1), pages 5-35, August.
    4. Mohammad Ali Beheshtinia & Parisa Feizollahy & Masood Fathi, 2021. "Supply Chain Optimization Considering Sustainability Aspects," Sustainability, MDPI, vol. 13(21), pages 1-23, October.
    5. Yuran Jin & Cheng Gao, 2023. "Hybrid Optimization of Green Supply Chain Network and Scheduling in Distributed 3D Printing Intelligent Factory," Sustainability, MDPI, vol. 15(7), pages 1-20, March.
    6. Guiliang Gong & Raymond Chiong & Qianwang Deng & Qiang Luo, 2020. "A memetic algorithm for multi-objective distributed production scheduling: minimizing the makespan and total energy consumption," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1443-1466, August.
    7. Po-Hsiang Lu & Muh-Cherng Wu & Hao Tan & Yong-Han Peng & Chen-Fu Chen, 2018. "A genetic algorithm embedded with a concise chromosome representation for distributed and flexible job-shop scheduling problems," Journal of Intelligent Manufacturing, Springer, vol. 29(1), pages 19-34, January.
    8. Wei Xiong & Dongmei Fu, 2018. "A new immune multi-agent system for the flexible job shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 857-873, April.
    9. Federica Costa & Matthias Thürer & Alberto Portioli-Staudacher, 2023. "Heterogeneous worker multi-functionality and efficiency in dual resource constrained manufacturing lines: an assessment by simulation," Operations Management Research, Springer, vol. 16(3), pages 1476-1489, September.
    10. Geurtsen, M. & Didden, Jeroen B.H.C. & Adan, J. & Atan, Z. & Adan, I., 2023. "Production, maintenance and resource scheduling: A review," European Journal of Operational Research, Elsevier, vol. 305(2), pages 501-529.
    11. Hao-Chin Chang & Tung-Kuan Liu, 2017. "Optimisation of distributed manufacturing flexible job shop scheduling by using hybrid genetic algorithms," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1973-1986, December.
    12. Arshad Ali & Yuvraj Gajpal & Tarek Y. Elmekkawy, 2021. "Distributed permutation flowshop scheduling problem with total completion time objective," OPSEARCH, Springer;Operational Research Society of India, vol. 58(2), pages 425-447, June.
    13. Rubén Jesús Pérez-López & María Mojarro-Magaña & Jesús Everardo Olguín-Tiznado & Claudia Camargo-Wilson & Juan Andrés López-Barreras & Julio Cesar Cano Gutiérrez & Jorge Luis Garcia-Alcaraz, 2022. "Planning, Execution, and Control of Operations in SC Activities—Baja California Manufacturing Case Study," Mathematics, MDPI, vol. 10(19), pages 1-19, September.
    14. Arash Amirteimoori & Reza Kia, 2023. "Concurrent scheduling of jobs and AGVs in a flexible job shop system: a parallel hybrid PSO-GA meta-heuristic," Flexible Services and Manufacturing Journal, Springer, vol. 35(3), pages 727-753, September.
    15. Nicolás Álvarez-Gil & Rafael Rosillo & David de la Fuente & Raúl Pino, 2021. "A discrete firefly algorithm for solving the flexible job-shop scheduling problem in a make-to-order manufacturing system," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(4), pages 1353-1374, December.
    16. Shijin Wang & Ming Liu, 2016. "Two-machine flow shop scheduling integrated with preventive maintenance planning," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(3), pages 672-690, February.
    17. J. Behnamian & S. M. T. Fatemi Ghomi, 2016. "A survey of multi-factory scheduling," Journal of Intelligent Manufacturing, Springer, vol. 27(1), pages 231-249, February.
    18. Yiyi Xu & M’hammed Sahnoun & Fouad Ben Abdelaziz & David Baudry, 2022. "A simulated multi-objective model for flexible job shop transportation scheduling," Annals of Operations Research, Springer, vol. 311(2), pages 899-920, April.
    19. 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.
    20. Li Zhou & Zhuoning Chen & Shaoping Chen, 2018. "An effective detailed operation scheduling in MES based on hybrid genetic algorithm," Journal of Intelligent Manufacturing, Springer, vol. 29(1), pages 135-153, January.

    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:gam:jsusta:v:14:y:2022:i:19:p:12120-:d:924739. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.