IDEAS home Printed from https://ideas.repec.org/a/spr/opsear/v60y2023i1d10.1007_s12597-021-00562-0.html
   My bibliography  Save this article

Multi-drill path sequencing models: A comparative study

Author

Listed:
  • Vijay Rathod

    (Governement Polytechnic)

Abstract

In multi-hole drilling, optimization of the drill-path sequencing can lead to a significant reduction in machining time and eventually improves productivity in industries. Hence, the researchers have explored this domain and the similarity between the structure of the multi-hole drilling process and the traveling salesman problem. Wherein the prime intent is to seek the shortest possible path to minimize the traveling distance. Further, the researchers have used mainly three types of distance functions to model the travel distance of drill tools i.e., Euclidean, rectilinear, and Chebyshev distances. This paper aims to study these distance function models and compares their performances and effect on the drill-path sequences using test problems from the literature. The Simulated Annealing algorithm is utilized for the optimization of the drill-path sequences. It also discusses the abilities and shortcomings of these distance functions.

Suggested Citation

  • Vijay Rathod, 2023. "Multi-drill path sequencing models: A comparative study," OPSEARCH, Springer;Operational Research Society of India, vol. 60(1), pages 554-570, March.
  • Handle: RePEc:spr:opsear:v:60:y:2023:i:1:d:10.1007_s12597-021-00562-0
    DOI: 10.1007/s12597-021-00562-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12597-021-00562-0
    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/s12597-021-00562-0?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. W. C. E. Lim & G. Kanagaraj & S. G. Ponnambalam, 2016. "A hybrid cuckoo search-genetic algorithm for hole-making sequence optimization," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 417-429, April.
    2. Hu, Luoke & Liu, Ying & Peng, Chen & Tang, Wangchujun & Tang, Renzhong & Tiwari, Ashutosh, 2018. "Minimising the energy consumption of tool change and tool path of machining by sequencing the features," Energy, Elsevier, vol. 147(C), pages 390-402.
    3. Sunny Diyaley & Abhiraj Aditya & Shankar Chakraborty, 2020. "Optimization of the multi-hole drilling path sequence for concentric circular patterns," OPSEARCH, Springer;Operational Research Society of India, vol. 57(3), pages 746-764, September.
    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. Xiao, Qinge & Li, Congbo & Tang, Ying & Pan, Jian & Yu, Jun & Chen, Xingzheng, 2019. "Multi-component energy modeling and optimization for sustainable dry gear hobbing," Energy, Elsevier, vol. 187(C).
    2. Sunny Diyaley & Abhiraj Aditya & Shankar Chakraborty, 2020. "Optimization of the multi-hole drilling path sequence for concentric circular patterns," OPSEARCH, Springer;Operational Research Society of India, vol. 57(3), pages 746-764, September.
    3. Sehyun Tak & Jeongyun Kim & Donghoun Lee, 2022. "Study on the Extraction Method of Sub-Network for Optimal Operation of Connected and Automated Vehicle-Based Mobility Service and Its Implication," Sustainability, MDPI, vol. 14(6), pages 1-28, March.
    4. Zhao, Junhua & Li, Li & Li, Lingling & Zhang, Yunfeng & Lin, Jiang & Cai, Wei & Sutherland, John W., 2023. "A multi-dimension coupling model for energy-efficiency of a machining process," Energy, Elsevier, vol. 274(C).
    5. Raghav Prasad Parouha & Pooja Verma, 2022. "An innovative hybrid algorithm for bound-unconstrained optimization problems and applications," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1273-1336, June.
    6. Yiying Zhang & Aining Chi, 2023. "Group teaching optimization algorithm with information sharing for numerical optimization and engineering optimization," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1547-1571, April.

    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:opsear:v:60:y:2023:i:1:d:10.1007_s12597-021-00562-0. 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.