IDEAS home Printed from https://ideas.repec.org/a/bcp/journl/v9y2025issue-9p8174-8182.html

Combinatorial Testing for Identifying Defect Patterns in Manufacturing

Author

Listed:
  • Maslita Abd Aziz

    (Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, Melaka, 76100, Malaysia)

  • Kamal Z. Zamli

    (Fakulti Komputeran, Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA), Pekan, 26600, Pahang, Malaysia)

  • Zuriani Mustaffa

    (Fakulti Komputeran, Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA), Pekan, 26600, Pahang, Malaysia)

Abstract

In the manufacturing industry, improving product quality and reducing defects are crucial objectives. This study investigates the use of combinatorial testing to analyse defect patterns in a manufacturing setting. We utilised a dataset containing various defects attributes on available open-source Kaggle datasets. Pairwise test cases were generated using hybrid metaheuristics to systematically explore interactions between these attributes. The proposed method significantly reduced the number of test cases while ensuring comprehensive coverage of pairwise interactions, compared to exhaustive testing. Results indicate that the combinatorial testing approach effectively identifies defect patterns, reducing the time span for defect identification. The integration of reward and penalty mechanisms with the Roulette Wheel algorithm in our hybrid metaheuristic optimisation process further enhanced the efficiency of candidate solutions for combinatorial testing. This study provides a practical framework for improving defect detection and quality control in manufacturing settings, highlighting the benefits of advanced combinatorial testing techniques.

Suggested Citation

  • Maslita Abd Aziz & Kamal Z. Zamli & Zuriani Mustaffa, 2025. "Combinatorial Testing for Identifying Defect Patterns in Manufacturing," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(9), pages 8174-8182, September.
    • Handle: RePEc:bcp:journl:v:9:y:2025:issue-9:p:8174-8182
    as

    Download full text from publisher

    File URL: https://www.rsisinternational.org/journals/ijriss/Digital-Library/volume-9-issue-9/8174-8182.pdf
    Download Restriction: no
    File URL: https://rsisinternational.org/journals/ijriss/articles/combinatorial-testing-for-identifying-defect-patterns-in-manufacturing/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Léo Baty & Kai Jungel & Patrick S. Klein & Axel Parmentier & Maximilian Schiffer, 2024. "Combinatorial Optimization-Enriched Machine Learning to Solve the Dynamic Vehicle Routing Problem with Time Windows," Transportation Science, INFORMS, vol. 58(4), pages 708-725, July.
    2. Christopher Hagedorn & Johannes Huegle & Rainer Schlosser, 2022. "Understanding unforeseen production downtimes in manufacturing processes using log data-driven causal reasoning," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 2027-2043, October.
    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. Paul, Aditya & Levin, Michael W. & Waller, S. Travis & Rey, David, 2025. "Data-driven optimization for drone delivery service planning with online demand," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 198(C).
    2. Vittorio Latorre & Donato Salvatore, 2026. "A Hybrid Genetic Search for Selective Routing with Overlapping Clusters: A Case Study in Order Picking," Journal of Heuristics, Springer, vol. 32(1), pages 1-52, March.
    3. Nguyen, Dang Viet Anh & Gunawan, Aldy & Misir, Mustafa & Hui, Lim Kwan & Vansteenwegen, Pieter, 2025. "Deep reinforcement learning for solving the stochastic e-waste collection problem," European Journal of Operational Research, Elsevier, vol. 327(1), pages 309-325.
    4. Zhou, Fangting & Lischka, Attila & Kulcsár, Balázs & Wu, Jiaming & Haghir Chehreghani, Morteza & Laporte, Gilbert, 2025. "Learning for routing: A guided review of recent developments and future directions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 202(C).
    5. Mancini, Simona & Ulmer, Marlin W. & Gansterer, Margaretha, 2025. "Dynamic assignment of delivery order bundles to in-store customers," Omega, Elsevier, vol. 133(C).
    6. Paradiso, Rosario & Roberti, Roberto & Ulmer, Marlin, 2025. "Lookahead scenario relaxation for dynamic time window assignment in service routing," Transportation Research Part B: Methodological, Elsevier, vol. 192(C).
    7. Richard Aviles-Lopez & Juan de Dios Luna del Castillo & Miguel Ángel Montero-Alonso, 2023. "Exploratory Matching Model Search Algorithm (EMMSA) for Causal Analysis: Application to the Cardboard Industry," Mathematics, MDPI, vol. 11(21), pages 1-34, October.

    More about this item

    Statistics

    Access and download statistics

    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:bcp:journl:v:9:y:2025:issue-9:p:8174-8182. 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: Dr. Pawan Verma (email available below). General contact details of provider: https://rsisinternational.org/journals/ijriss/ .

    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.