IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v31y2020i5d10.1007_s10845-019-01496-7.html
   My bibliography  Save this article

Hybrid constrained permutation algorithm and genetic algorithm for process planning problem

Author

Listed:
  • Abdullah Falih

    (University of Baghdad)

  • Ahmed Z. M. Shammari

    (University of Baghdad)

Abstract

In this research, a hybrid constrained permutation algorithm and genetic algorithm approach is proposed to solve the process planning problem and to facilitate the optimisation process. In this approach, the process planning problem is represented as a graph in which operations are clustered corresponding to their machine, tool, and tool access direction similarities. A constrained permutation algorithm (CPA) developed to generate a set of optimised feasible operations sequences based on the principles of minimising the number of setup changes and the number of tool changes. Due to its strong capability in global search through multiple optima, genetic algorithm (GA) is used to search for an optimal or near optimal process plan, in which the population is initialised according to the operations sequences generated by CPA. Furthermore, to prevent premature convergence to local optima, a mixed crossover operator is designed and equipped into GA. Four comparative case studies are carried out to evidence the feasibility and robustness of the proposed CPAGA approach against GA, simulated annealing, tabu search, ant colony optimisation, and particle swarm optimisation based approaches reported in the literature, and the results are promising.

Suggested Citation

  • Abdullah Falih & Ahmed Z. M. Shammari, 2020. "Hybrid constrained permutation algorithm and genetic algorithm for process planning problem," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1079-1099, June.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:5:d:10.1007_s10845-019-01496-7
    DOI: 10.1007/s10845-019-01496-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-019-01496-7
    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/s10845-019-01496-7?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. Yuliang Su & Xuening Chu & Dongping Chen & Xiwu Sun, 2018. "A genetic algorithm for operation sequencing in CAPP using edge selection based encoding strategy," Journal of Intelligent Manufacturing, Springer, vol. 29(2), pages 313-332, February.
    2. Moon, Chiung & Kim, Jongsoo & Choi, Gyunghyun & Seo, Yoonho, 2002. "An efficient genetic algorithm for the traveling salesman problem with precedence constraints," European Journal of Operational Research, Elsevier, vol. 140(3), pages 606-617, August.
    3. Jianping Dou & Jun Li & Chun Su, 2018. "A discrete particle swarm optimisation for operation sequencing in CAPP," International Journal of Production Research, Taylor & Francis Journals, vol. 56(11), pages 3795-3814, June.
    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. Konstantinos S. Boulas & Georgios D. Dounias & Chrissoleon T. Papadopoulos, 2023. "A hybrid evolutionary algorithm approach for estimating the throughput of short reliable approximately balanced production lines," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 823-852, February.
    2. Qihao Liu & Xinyu Li & Liang Gao, 2021. "Mathematical modeling and a hybrid evolutionary algorithm for process planning," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 781-797, March.

    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. Luo, Kaiping & Shen, Guangya & Li, Liheng & Sun, Jianfei, 2023. "0-1 mathematical programming models for flexible process planning," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1160-1175.
    2. Yuming Guo, 2023. "Towards the efficient generation of variant design in product development networks: network nodes importance based product configuration evaluation approach," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 615-631, February.
    3. Carter, Arthur E. & Ragsdale, Cliff T., 2009. "Quality inspection scheduling for multi-unit service enterprises," European Journal of Operational Research, Elsevier, vol. 194(1), pages 114-126, April.
    4. Nikolakopoulos, Athanassios & Sarimveis, Haralambos, 2007. "A threshold accepting heuristic with intense local search for the solution of special instances of the traveling salesman problem," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1911-1929, March.
    5. Chunghun Ha, 2020. "Evolving ant colony system for large-sized integrated process planning and scheduling problem considering sequence-dependent setup times," Flexible Services and Manufacturing Journal, Springer, vol. 32(3), pages 523-560, September.
    6. Mina Roohnavazfar & Seyed Hamid Reza Pasandideh, 2022. "Decomposition algorithm for the multi-trip single vehicle routing problem with AND-type precedence constraints," Operational Research, Springer, vol. 22(4), pages 4253-4285, September.
    7. Qin, Hu & Zhang, Zizhen & Qi, Zhuxuan & Lim, Andrew, 2014. "The freight consolidation and containerization problem," European Journal of Operational Research, Elsevier, vol. 234(1), pages 37-48.
    8. Carter, Arthur E. & Ragsdale, Cliff T., 2006. "A new approach to solving the multiple traveling salesperson problem using genetic algorithms," European Journal of Operational Research, Elsevier, vol. 175(1), pages 246-257, November.
    9. Yajun Yang & Zhongfei Li & Xin Wang & Qinghua Hu, 2019. "Finding the Shortest Path with Vertex Constraint over Large Graphs," Complexity, Hindawi, vol. 2019, pages 1-13, February.
    10. Yuliang Su & Xuening Chu & Dongping Chen & Xiwu Sun, 2018. "A genetic algorithm for operation sequencing in CAPP using edge selection based encoding strategy," Journal of Intelligent Manufacturing, Springer, vol. 29(2), pages 313-332, February.
    11. Merve Kayacı Çodur & Mustafa Yılmaz, 2020. "A time-dependent hierarchical Chinese postman problem," 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. 28(1), pages 337-366, March.
    12. Izabela Nielsen & Quang-Vinh Dang & Grzegorz Bocewicz & Zbigniew Banaszak, 2017. "A methodology for implementation of mobile robot in adaptive manufacturing environments," Journal of Intelligent Manufacturing, Springer, vol. 28(5), pages 1171-1188, June.
    13. Hyun Cheol Lee & Chunghun Ha, 2019. "Sustainable Integrated Process Planning and Scheduling Optimization Using a Genetic Algorithm with an Integrated Chromosome Representation," Sustainability, MDPI, vol. 11(2), pages 1-23, January.
    14. Gustavo Erick Anaya Fuentes & Eva Selene Hernández Gress & Juan Carlos Seck Tuoh Mora & Joselito Medina Marín, 2018. "Solution to travelling salesman problem by clusters and a modified multi-restart iterated local search metaheuristic," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-20, August.
    15. Kaj Holmberg, 2019. "The (Over)zealous Snow Remover Problem," Transportation Science, INFORMS, vol. 53(3), pages 867-881, May.

    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:31:y:2020:i:5:d:10.1007_s10845-019-01496-7. 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.