IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i11p4690-d368935.html
   My bibliography  Save this article

Computing the Assembly Guidance for Maximizing Product Quality in the Virtual Assembly

Author

Listed:
  • Chen-Kun Tsung

    (Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, No. 57, Sec. 2, Zhongshan Rd., Taiping Dist., Taichung 41170, Taiwan)

  • Tseng-Fung Ho

    (Department of Industrial Engineering and Management, National Chin-Yi University of Technology, No. 57, Sec. 2, Zhongshan Rd., Taiping Dist., Taichung 41170, Taiwan)

  • Hsuan-Yu Huang

    (Mechanical and Mechatronics System Research Labs, Industrial Technology Research Institute, No. 195, Sec. 4, Chung Hsing Rd., Chutung, Hsinchu 31040, Taiwan)

  • Shu-Hui Yang

    (Mechanical and Mechatronics System Research Labs, Industrial Technology Research Institute, No. 195, Sec. 4, Chung Hsing Rd., Chutung, Hsinchu 31040, Taiwan)

  • Po-Nien Tsou

    (Mechanical and Mechatronics System Research Labs, Industrial Technology Research Institute, No. 195, Sec. 4, Chung Hsing Rd., Chutung, Hsinchu 31040, Taiwan)

  • Ming-Cheng Tsai

    (Mechanical and Mechatronics System Research Labs, Industrial Technology Research Institute, No. 195, Sec. 4, Chung Hsing Rd., Chutung, Hsinchu 31040, Taiwan)

  • Yi-Ping Huang

    (Mechanical and Mechatronics System Research Labs, Industrial Technology Research Institute, No. 195, Sec. 4, Chung Hsing Rd., Chutung, Hsinchu 31040, Taiwan)

Abstract

Assembly is the final process of manufacturing, and a good assembly plan reduces the effect of the tolerance generated in the early stages by the tolerance elimination. In the current assembly lines, the assemblers pick up the workpieces and install them together by the assembly instructions. When the workpieces are oversize or undersize, the product can not be installed correctly. Therefore, the assembler considers the secondary processing to fix the tolerance and then installs them together again. The product could be installed, but the product quality may be reduced by the secondary process. So, we formulate the assembly process as a combinatorial optimization problem, named by the dimensional chain assembly (DCA) problem. Given some workpieces with the corresponding actual size, computing the assembly guidance is the goal of the DCA problem, and the product quality is applied to represent the solution quality. The assemblers follow the assembly guidance to install the products. We firstly prove that the DCA problem is NP-complete and collect the requirements of solving the DCA problem from the implementation perspective: the sustainability, the minimization of computation time, and the guarantee of product quality. We consider solution refinement and the solution property inheritance of the single-solution evolution approach to discover and refine the quality of the assembly guidance. Based on the above strategies, we propose the assembly guidance optimizer (AGO) based on the simulated annealing algorithm to compute the assembly guidance. From the simulation results, the AGO reaches all requirements of the DCA problem. The variance of the computation time and the solution quality is related to the problem scale linearly, so the computation time and the solution quality can be estimated by the problem scale. Moreover, increasing the search breadth is unnecessary for improving the solution quality. In summary, the proposed AGO satisfies with the necessaries of the sustainability, the minimization of computation time, and the guarantee of product quality for the requirements of the DCA, and it can be considered in the real-world applications.

Suggested Citation

  • Chen-Kun Tsung & Tseng-Fung Ho & Hsuan-Yu Huang & Shu-Hui Yang & Po-Nien Tsou & Ming-Cheng Tsai & Yi-Ping Huang, 2020. "Computing the Assembly Guidance for Maximizing Product Quality in the Virtual Assembly," Sustainability, MDPI, vol. 12(11), pages 1-14, June.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:11:p:4690-:d:368935
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/11/4690/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/11/4690/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pawel Sitek & Jarosław Wikarek, 2019. "Capacitated vehicle routing problem with pick-up and alternative delivery (CVRPPAD): model and implementation using hybrid approach," Annals of Operations Research, Springer, vol. 273(1), pages 257-277, February.
    2. Helena Ramalhinho Lourenço & Olivier C. Martin & Thomas Stützle, 2019. "Iterated Local Search: Framework and Applications," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, edition 3, chapter 0, pages 129-168, Springer.
    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. Pardis Pourmohammadi & Reza Tavakkoli-Moghaddam & Yaser Rahimi & Chefi Triki, 2023. "Solving a hub location-routing problem with a queue system under social responsibility by a fuzzy meta-heuristic algorithm," Annals of Operations Research, Springer, vol. 324(1), pages 1099-1128, May.
    2. Fowler, John W. & Mönch, Lars, 2022. "A survey of scheduling with parallel batch (p-batch) processing," European Journal of Operational Research, Elsevier, vol. 298(1), pages 1-24.
    3. Alvarez, Aldair & Miranda, Pedro & Rohmer, S.U.K., 2022. "Production routing for perishable products," Omega, Elsevier, vol. 111(C).
    4. Paredes-Belmar, Germán & Montero, Elizabeth & Lüer-Villagra, Armin & Marianov, Vladimir & Araya-Sassi, Claudio, 2022. "Vehicle routing for milk collection with gradual blending: A case arising in Chile," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1403-1416.
    5. Calmels, Dorothea, 2022. "An iterated local search procedure for the job sequencing and tool switching problem with non-identical parallel machines," European Journal of Operational Research, Elsevier, vol. 297(1), pages 66-85.
    6. Eduardo Queiroga & Rian G. S. Pinheiro & Quentin Christ & Anand Subramanian & Artur A. Pessoa, 2021. "Iterated local search for single machine total weighted tardiness batch scheduling," Journal of Heuristics, Springer, vol. 27(3), pages 353-438, June.
    7. Xiao Pan & Dongna Lu & Ning Li, 2024. "Route Planning of Supermarket Delivery through Third-Party Logistics Considering Carbon Emission Cost," Mathematics, MDPI, vol. 12(7), pages 1-19, March.
    8. Mancini, Simona & Gansterer, Margaretha & Triki, Chefi, 2023. "Locker box location planning under uncertainty in demand and capacity availability," Omega, Elsevier, vol. 120(C).
    9. D. G. N. D. Jayarathna & G. H. J. Lanel & Z. A. M. S. Juman, 2022. "Industrial vehicle routing problem: a case study," Journal of Shipping and Trade, Springer, vol. 7(1), pages 1-27, December.
    10. Christian Tilk & Katharina Olkis & Stefan Irnich, 2021. "The last-mile vehicle routing problem with delivery options," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(4), pages 877-904, December.
    11. Nima Pourmohammadreza & Mohammad Reza Akbari Jokar, 2023. "A Novel Two-Phase Approach for Optimization of the Last-Mile Delivery Problem with Service Options," Sustainability, MDPI, vol. 15(10), pages 1-25, May.
    12. Vincent F. Yu & Hadi Susanto & Yu-Hsuan Yeh & Shih-Wei Lin & Yu-Tsung Huang, 2022. "The Vehicle Routing Problem with Simultaneous Pickup and Delivery and Parcel Lockers," Mathematics, MDPI, vol. 10(6), pages 1-22, March.
    13. Vincent F. Yu & Grace Aloina & Hadi Susanto & Mohammad Khoirul Effendi & Shih-Wei Lin, 2022. "Regional Location Routing Problem for Waste Collection Using Hybrid Genetic Algorithm-Simulated Annealing," Mathematics, MDPI, vol. 10(12), pages 1-23, June.
    14. Albert Einstein Fernandes Muritiba & Tibérius O. Bonates & Stênio Oliveira Da Silva & Manuel Iori, 2021. "Branch-and-Cut and Iterated Local Search for the Weighted k -Traveling Repairman Problem: An Application to the Maintenance of Speed Cameras," Transportation Science, INFORMS, vol. 55(1), pages 139-159, 1-2.
    15. Firoozeh Kaveh & Reza Tavakkoli-Moghaddam & Chefi Triki & Yaser Rahimi & Amin Jamili, 2021. "A new bi-objective model of the urban public transportation hub network design under uncertainty," Annals of Operations Research, Springer, vol. 296(1), pages 131-162, January.
    16. Amira Saker & Amr Eltawil & Islam Ali, 2023. "Adaptive Large Neighborhood Search Metaheuristic for the Capacitated Vehicle Routing Problem with Parcel Lockers," Logistics, MDPI, vol. 7(4), pages 1-27, October.
    17. Yu, Vincent F. & Jodiawan, Panca & Redi, A.A.N. Perwira, 2022. "Crowd-shipping problem with time windows, transshipment nodes, and delivery options," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    18. Zhaofang Mao & Dian Huang & Kan Fang & Chengbo Wang & Dandan Lu, 2020. "Milk-run routing problem with progress-lane in the collection of automobile parts," Annals of Operations Research, Springer, vol. 291(1), pages 657-684, August.
    19. Jasmin Grabenschweiger & Karl F. Doerner & Richard F. Hartl & Martin W. P. Savelsbergh, 2021. "The vehicle routing problem with heterogeneous locker boxes," 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(1), pages 113-142, March.
    20. Máximo, Vinícius R. & Nascimento, Mariá C.V., 2021. "A hybrid adaptive iterated local search with diversification control to the capacitated vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 294(3), pages 1108-1119.

    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:12:y:2020:i:11:p:4690-:d:368935. 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.