IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i23p4607-d994021.html
   My bibliography  Save this article

An Effective 4–Phased Framework for Scheduling Job-Shop Manufacturing Systems Using Weighted NSGA-II

Author

Listed:
  • Aidin Delgoshaei

    (Department of Mechanical and Manufacturing Engineering, Universiti Putra Malaysia, Serdang 43400, SL, Malaysia)

  • Mohd Khairol Anuar Bin Mohd Ariffin

    (Department of Mechanical and Manufacturing Engineering, Universiti Putra Malaysia, Serdang 43400, SL, Malaysia)

  • Zulkiflle B. Leman

    (Department of Mechanical and Manufacturing Engineering, Universiti Putra Malaysia, Serdang 43400, SL, Malaysia)

Abstract

Improving the performance of manufacturing systems is a vital issue in today’s rival market. For this purpose, during the last decade, scientists have considered more than one objective function while scheduling a production line. This paper develops a 4-phased fuzzy framework to identify effective factors, determine their weights on multi-objective functions, and, accordingly, schedule manufacturing systems in a fuzzy environment. The aim is to optimize product completion time and operational and product defect costs in a job-shop-based multi-objective fuzzy scheduling problem. In the first and second phases of the proposed framework, it was shown that the existing uncertainty of the internal factors for the studied cases causes the weights of factors to change up to 44.5%. Then, a fuzzy-weighted NSGA-II is proposed (FW-NSGA-II) to address the developed Non-linear Fuzzy Multi-objective Dual resource-constrained scheduling problem. Comparing the outcomes of the proposed method with other solving algorithms, such as the Sine Cosine Algorithm, Simulated Annealing, Tabu Search, and TLBO heuristic, using seven series of comprehensive computational experiments, indicates the superiority of the proposed framework in scheduling manufacturing systems. The outcomes indicated that using the proposed method for the studied cases saved up to 5% in the objective function for small-scale, 11.2% for medium-scale, and 3.8% for large-scale manufacturing systems. The outcomes of this study can help production planning managers to provide more realistic schedules by considering fuzzy factors in their manufacturing systems. Further investigating the proposed method for dynamic product conditions is another direction for future research.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:23:p:4607-:d:994021
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/23/4607/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/23/4607/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fahad Kh. A.O.H. Alazemi & Mohd Khairol Anuar Bin Mohd Ariffin & Faizal Bin Mustapha & Eris Elianddy bin Supeni, 2021. "A Comprehensive Fuzzy Decision-Making Method for Minimizing Completion Time in Manufacturing Process in Supply Chains," Mathematics, MDPI, vol. 9(22), pages 1-39, November.
    2. Dominik Kress & David Müller & Jenny Nossack, 2019. "A worker constrained flexible job shop scheduling problem with sequence-dependent setup times," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(1), pages 179-217, March.
    3. Xiuli Wu & Junjian Peng & Xiao Xiao & Shaomin Wu, 2021. "An effective approach for the dual-resource flexible job shop scheduling problem considering loading and unloading," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 707-728, March.
    4. Melissa Shahgholi Zadeh & Yalda Katebi & Ali Doniavi, 2019. "A heuristic model for dynamic flexible job shop scheduling problem considering variable processing times," International Journal of Production Research, Taylor & Francis Journals, vol. 57(10), pages 3020-3035, May.
    5. 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.
    6. 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.
    7. Imen Khettabi & Lyes Benyoucef & Mohamed Amine Boutiche, 2022. "Sustainable multi-objective process planning in reconfigurable manufacturing environment: adapted new dynamic NSGA-II vs New NSGA-III," International Journal of Production Research, Taylor & Francis Journals, vol. 60(20), pages 6329-6349, October.
    8. Sun, Yige & Chung, Sai-Ho & Wen, Xin & Ma, Hoi-Lam, 2021. "Novel robotic job-shop scheduling models with deadlock and robot movement considerations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    9. 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.
    10. Aidin Delgoshaei & Mohd Khairol Anuar Ariffin & Ahad Ali, 2017. "A multi-period scheduling method for trading-off between skilled-workers allocation and outsource service usage in dynamic CMS," International Journal of Production Research, Taylor & Francis Journals, vol. 55(4), pages 997-1039, February.
    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. 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.
    2. Alejandro Vital-Soto & Mohammed Fazle Baki & Ahmed Azab, 2023. "A multi-objective mathematical model and evolutionary algorithm for the dual-resource flexible job-shop scheduling problem with sequencing flexibility," Flexible Services and Manufacturing Journal, Springer, vol. 35(3), pages 626-668, September.
    3. Mei Li & Gai-Ge Wang & Helong Yu, 2021. "Sorting-Based Discrete Artificial Bee Colony Algorithm for Solving Fuzzy Hybrid Flow Shop Green Scheduling Problem," Mathematics, MDPI, vol. 9(18), pages 1-30, September.
    4. 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.
    5. 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).
    6. 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.
    7. Chen, Ziyue & Huang, Lizhen, 2021. "Digital twins for information-sharing in remanufacturing supply chain: A review," Energy, Elsevier, vol. 220(C).
    8. Cildoz, Marta & Ibarra, Amaia & Mallor, Fermin, 2020. "Coping with stress in emergency department physicians through improved patient-flow management," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    9. Wenkang Zhang & Yufan Zheng & Rafiq Ahmad, 2023. "The integrated process planning and scheduling of flexible job-shop-type remanufacturing systems using improved artificial bee colony algorithm," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 2963-2988, October.
    10. 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.
    11. 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.
    12. Sun, Xuting & Kuo, Yong-Hong & Xue, Weili & Li, Yanzhi, 2024. "Technology-driven logistics and supply chain management for societal impacts," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 185(C).
    13. 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.
    14. 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.
    15. Chen, Wanying (Amanda) & De Koster, René & Gong, Yeming, 2023. "Warehouses without aisles: Layout design of a multi-deep rack climbing robotic system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    16. Zhijie Huang & Lin Huang & Debiao Li, 2024. "Co-Evolutionary Algorithm for Two-Stage Hybrid Flow Shop Scheduling Problem with Suspension Shifts," Mathematics, MDPI, vol. 12(16), pages 1-30, August.
    17. Monaci, Marta & Agasucci, Valerio & Grani, Giorgio, 2024. "An actor-critic algorithm with policy gradients to solve the job shop scheduling problem using deep double recurrent agents," European Journal of Operational Research, Elsevier, vol. 312(3), pages 910-926.
    18. Gabriel Mauricio Zambrano-Rey & Eliana María González-Neira & Gabriel Fernando Forero-Ortiz & María José Ocampo-Monsalve & Andrea Rivera-Torres, 2024. "Minimizing the expected maximum lateness for a job shop subject to stochastic machine breakdowns," Annals of Operations Research, Springer, vol. 338(1), pages 801-833, July.
    19. Vivek Warke & Satish Kumar & Arunkumar Bongale & Ketan Kotecha, 2021. "Sustainable Development of Smart Manufacturing Driven by the Digital Twin Framework: A Statistical Analysis," Sustainability, MDPI, vol. 13(18), pages 1-49, September.
    20. Hu, Yue & Yang, Hongbing & Huang, Yi, 2022. "Conflict-free scheduling of large-scale multi-load AGVs in material transportation network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).

    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:jmathe:v:10:y:2022:i:23:p:4607-:d:994021. 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.