IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i20p4384-d1264636.html
   My bibliography  Save this article

An Improved Mayfly Optimization Algorithm for Type-2 Multi-Objective Integrated Process Planning and Scheduling

Author

Listed:
  • Ke Yang

    (College of Mathematic and Information, China West Normal University, Nanchong 637009, China)

  • Dazhi Pan

    (College of Mathematic and Information, China West Normal University, Nanchong 637009, China
    Sichuan Colleges and Universities Key Laboratory of Optimization Theory and Applications, Nanchong 637009, China)

Abstract

The type-2 multi-objective integrated process planning and scheduling problem, as an NP-hard problem, is required to deal with both process planning and job shop scheduling, and to generate optimal schedules while planning optimal machining paths for the workpieces. For the type-2 multi-objective integrated process planning and scheduling problem, a mathematical model with the minimization objectives of makespan, total machine load, and critical machine load is developed. A multi-objective mayfly optimization algorithm with decomposition and adaptive neighborhood search is designed to solve this problem. The algorithm uses two forms of encoding, a transformation scheme designed to allow the two codes to switch between each other during evolution, and a hybrid population initialization strategy designed to improve the quality of the initial solution while taking into account diversity. In addition, an adaptive neighborhood search cycle based on the average distance of the Pareto optimal set to the ideal point is designed to improve the algorithm’s merit-seeking ability while maintaining the diversity of the population. The proposed encoding and decoding scheme can better transform the continuous optimization algorithm to apply to the combinatorial optimization problem. Finally, it is experimentally verified that the proposed algorithm achieves better experimental results and can effectively deal with type-2 MOIPPS.

Suggested Citation

  • Ke Yang & Dazhi Pan, 2023. "An Improved Mayfly Optimization Algorithm for Type-2 Multi-Objective Integrated Process Planning and Scheduling," Mathematics, MDPI, vol. 11(20), pages 1-19, October.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:20:p:4384-:d:1264636
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/20/4384/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/20/4384/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Liangliang Jin & Qiuhua Tang & Chaoyong Zhang & Xinyu Shao & Guangdong Tian, 2016. "More MILP models for integrated process planning and scheduling," International Journal of Production Research, Taylor & Francis Journals, vol. 54(14), pages 4387-4402, July.
    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. Zhu, Xuedong & Son, Junbo & Zhang, Xi & Wu, Jianguo, 2023. "Constraint programming and logic-based Benders decomposition for the integrated process planning and scheduling problem," Omega, Elsevier, vol. 117(C).
    2. Jin Huang & Liangliang Jin & Chaoyong Zhang, 2017. "Mathematical Modeling and a Hybrid NSGA-II Algorithm for Process Planning Problem Considering Machining Cost and Carbon Emission," Sustainability, MDPI, vol. 9(10), pages 1-18, September.
    3. Barzanji, Ramin & Naderi, Bahman & Begen, Mehmet A., 2020. "Decomposition algorithms for the integrated process planning and scheduling problem," Omega, Elsevier, vol. 93(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:11:y:2023:i:20:p:4384-:d:1264636. 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.