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Enhancing operational research in mechatronic systems via modularization: comparative analysis of four clustering algorithms using validation indices

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  • Ioannis Mikrou

    (University of Western Macedonia)

  • Nickolas S. Sapidis

    (University of Western Macedonia
    Hellenic Open University)

Abstract

Modularization is one of the most robust methods that industries use to profit. This technique allows Operational Research to manage complex systems by efficiently dividing them into smaller ones and thus lowering the affiliated risks and costs. Mechatronic products are complex systems associated with diverse disciplines, laborious to compose and decompose, and can benefit from modularization. In this research, Partitioning Around Medoids (PAM), Ward’s method, Divisive ANAlysis (DIANA), and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithms are utilized in combination with Design Structure Matrices (DSM) to cluster 175 test subjects, and their results are compared using four validation techniques. Agglomerative Coefficient (AC), Divisive Coefficient (DC), Silhouette Coefficient (SC), Composed Density between and within clusters (CDbw), and the visual inspection of two-dimensional representations of each algorithm's clustering results are the validation techniques used in this research to find the most suitable algorithm for clustering such intricate systems. Additionally, other data that emerged from this research, such as time complexity, total execution time, and average RAM usage, are also used to evaluate the overall performance of each clustering algorithm.

Suggested Citation

  • Ioannis Mikrou & Nickolas S. Sapidis, 2024. "Enhancing operational research in mechatronic systems via modularization: comparative analysis of four clustering algorithms using validation indices," Operational Research, Springer, vol. 24(4), pages 1-44, December.
    • Handle: RePEc:spr:operea:v:24:y:2024:i:4:d:10.1007_s12351-024-00872-3
    DOI: 10.1007/s12351-024-00872-3
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    References listed on IDEAS

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    1. Mayra Z Rodriguez & Cesar H Comin & Dalcimar Casanova & Odemir M Bruno & Diego R Amancio & Luciano da F Costa & Francisco A Rodrigues, 2019. "Clustering algorithms: A comparative approach," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-34, January.
    2. Katsikopoulos, Konstantinos V. & Durbach, Ian N. & Stewart, Theodor J., 2018. "When should we use simple decision models? A synthesis of various research strands," Omega, Elsevier, vol. 81(C), pages 17-25.
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    1. Ioannis Mikrou & Nickolas S. Sapidis, 2025. "A Systematic Evaluation of Clustering Algorithms Against Expert-Derived Clustering," SN Operations Research Forum, Springer, vol. 6(2), pages 1-24, June.

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