IDEAS home Printed from https://ideas.repec.org/a/spr/operea/v22y2022i4d10.1007_s12351-021-00682-x.html
   My bibliography  Save this article

A two-phase part family formation model to optimize resource planning: a case study in the electronics industry

Author

Listed:
  • Imen Zaabar

    (École de technologie supérieure)

  • Vladimir Polotski

    (École de technologie supérieure)

  • Léon Bérard

    (IBM)

  • Boujemaa El-Ouaqaf

    (IBM)

  • Yvan Beauregard

    (École de technologie supérieure)

  • Marc Paquet

    (École de technologie supérieure)

Abstract

While clustering is a powerful methodology used for grouping objects into families, it is hard to conceive a natural object grouping method without considering the context of a particular application. In high-dimensional problems with large volumes and rapidly evolving part flows, many clustering methods have traditionally been used to form part families considering similarities. In this paper, a two-phase clustering method is developed to optimize the grouping process under technological constraints, in a bid to improve resource planning. The first phase consists in forming part families using the Agglomerative Hierarchical Clustering approach, considering multidimensional parametrization of the part, while in the second phase, the optimal number of clusters is determined using ELECTRE III, which serves to handle uncertainty. Based on a real case study in the electronics industry, an improved production planning solution is proposed to validate the method’s efficiency. The solution was compared to the in-use method to highlight its added value.

Suggested Citation

  • Imen Zaabar & Vladimir Polotski & Léon Bérard & Boujemaa El-Ouaqaf & Yvan Beauregard & Marc Paquet, 2022. "A two-phase part family formation model to optimize resource planning: a case study in the electronics industry," Operational Research, Springer, vol. 22(4), pages 4441-4469, September.
    • Handle: RePEc:spr:operea:v:22:y:2022:i:4:d:10.1007_s12351-021-00682-x
    DOI: 10.1007/s12351-021-00682-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12351-021-00682-x
    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/s12351-021-00682-x?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. Tamal Ghosh & Pranab K. Dan, 2012. "Particle swarm optimisation in development of component families using classification and coding system: a case study in an Indian manufacturing firm," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 13(4), pages 441-456.
    2. N. Megala & Chandrasekharan Rajendran, 2013. "An improved ant-colony algorithm for the grouping of machine-cells and part-families in cellular manufacturing systems," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 17(3), pages 345-373.
    3. Aalaei, Amin & Davoudpour, Hamid, 2017. "A robust optimization model for cellular manufacturing system into supply chain management," International Journal of Production Economics, Elsevier, vol. 183(PC), pages 667-679.
    4. Faisal Hasan & P.K. Jain, 2016. "A neural network-based approach for part family classification for a reconfigurable manufacturing system," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 25(2), pages 143-168.
    5. A. Baykasoglu & L. Gorkemli, 2015. "Agent-based dynamic part family formation for cellular manufacturing applications," International Journal of Production Research, Taylor & Francis Journals, vol. 53(3), pages 774-792, February.
    6. Büchi, Giacomo & Cugno, Monica & Castagnoli, Rebecca, 2020. "Smart factory performance and Industry 4.0," Technological Forecasting and Social Change, Elsevier, vol. 150(C).
    7. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    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. Alicja Grześkowiak, 2016. "Assessment of Participation in Cultural Activities in Poland by Selected Multivariate Methods," European Journal of Social Sciences Education and Research Articles, Revistia Research and Publishing, vol. 3, January -.
    2. Yunpeng Zhao & Qing Pan & Chengan Du, 2019. "Logistic regression augmented community detection for network data with application in identifying autism‐related gene pathways," Biometrics, The International Biometric Society, vol. 75(1), pages 222-234, March.
    3. Wu, Han-Ming & Tien, Yin-Jing & Chen, Chun-houh, 2010. "GAP: A graphical environment for matrix visualization and cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 767-778, March.
    4. José E. Chacón, 2021. "Explicit Agreement Extremes for a 2 × 2 Table with Given Marginals," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 257-263, July.
    5. F. Marta L. Di Lascio & Andrea Menapace & Roberta Pappadà, 2024. "A spatially‐weighted AMH copula‐based dissimilarity measure for clustering variables: An application to urban thermal efficiency," Environmetrics, John Wiley & Sons, Ltd., vol. 35(1), February.
    6. Yifan Zhu & Chongzhi Di & Ying Qing Chen, 2019. "Clustering Functional Data with Application to Electronic Medication Adherence Monitoring in HIV Prevention Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(2), pages 238-261, July.
    7. Irene Vrbik & Paul McNicholas, 2015. "Fractionally-Supervised Classification," Journal of Classification, Springer;The Classification Society, vol. 32(3), pages 359-381, October.
    8. Maurizio Vichi & Carlo Cavicchia & Patrick J. F. Groenen, 2022. "Hierarchical Means Clustering," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 553-577, November.
    9. Gilberto Santos & Jose Carlos Sá & Maria João Félix & Luís Barreto & Filipe Carvalho & Manuel Doiro & Kristína Zgodavová & Miladin Stefanović, 2021. "New Needed Quality Management Skills for Quality Managers 4.0," Sustainability, MDPI, vol. 13(11), pages 1-22, May.
    10. Federica Murmura & Laura Bravi & Gilberto Santos, 2021. "Sustainable Process and Product Innovation in the Eyewear Sector: The Role of Industry 4.0 Enabling Technologies," Sustainability, MDPI, vol. 13(1), pages 1-16, January.
    11. Batool, Fatima & Hennig, Christian, 2021. "Clustering with the Average Silhouette Width," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    12. Patrick D. Shay & Stephen S. Farnsworth Mick, 2017. "Clustered and distinct: a taxonomy of local multihospital systems," Health Care Management Science, Springer, vol. 20(3), pages 303-315, September.
    13. Roberto Rocci & Stefano Antonio Gattone & Roberto Di Mari, 2018. "A data driven equivariant approach to constrained Gaussian mixture modeling," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(2), pages 235-260, June.
    14. Wan-Lun Wang, 2019. "Mixture of multivariate t nonlinear mixed models for multiple longitudinal data with heterogeneity and missing values," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 196-222, March.
    15. Matthijs Warrens, 2010. "Inequalities Between Kappa and Kappa-Like Statistics for k×k Tables," Psychometrika, Springer;The Psychometric Society, vol. 75(1), pages 176-185, March.
    16. Redivo, Edoardo & Nguyen, Hien D. & Gupta, Mayetri, 2020. "Bayesian clustering of skewed and multimodal data using geometric skewed normal distributions," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    17. Zander, Bennet & Lange, Kerstin & Haasis, Hans-Dietrich, 2020. "Impacts of a smart factory on procurement logistics," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Data Science and Innovation in Supply Chain Management: How Data Transforms the Value Chain. Proceedings of the Hamburg International Conference of Lo, volume 29, pages 459-485, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    18. Jerzy Korzeniewski, 2016. "New Method Of Variable Selection For Binary Data Cluster Analysis," Statistics in Transition New Series, Polish Statistical Association, vol. 17(2), pages 295-304, June.
    19. Zhu, Xuwen & Melnykov, Volodymyr, 2018. "Manly transformation in finite mixture modeling," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 190-208.
    20. Li, Pai-Ling & Chiou, Jeng-Min, 2011. "Identifying cluster number for subspace projected functional data clustering," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2090-2103, June.

    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:operea:v:22:y:2022:i:4:d:10.1007_s12351-021-00682-x. 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.