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

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Publicado en:Operational Research vol. 24, no. 4 (Dec 2024), p. 63
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Springer Nature B.V.
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245 1 |a Enhancing operational research in mechatronic systems via modularization: comparative analysis of four clustering algorithms using validation indices 
260 |b Springer Nature B.V.  |c Dec 2024 
513 |a Journal Article 
520 3 |a 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. 
653 |a Complex systems 
653 |a Modularization 
653 |a Algorithms 
653 |a Complexity 
653 |a Clustering 
653 |a Density 
653 |a Cost benefit analysis 
653 |a Validation studies 
653 |a Comparative analysis 
653 |a Mechatronics 
653 |a Operations research 
773 0 |t Operational Research  |g vol. 24, no. 4 (Dec 2024), p. 63 
786 0 |d ProQuest  |t ABI/INFORM Global 
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