A comparison of different clustering algorithms for the project time buffering problem
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| Titel: | A comparison of different clustering algorithms for the project time buffering problem |
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| Autoren: | Cao, Fangfang, Servranckx, Tom, Vanhoucke, Mario, He, Zhengwen |
| Quelle: | COMPUTERS & INDUSTRIAL ENGINEERING ; ISSN: 0360-8352 ; ISSN: 1879-0550 |
| Publikationsjahr: | 2025 |
| Bestand: | Ghent University Academic Bibliography |
| Schlagwörter: | Business and Economics, Clustering, Project scheduling, Time buffering problem, Multifactorial evolutionary algorithm |
| Beschreibung: | This paper studies the decentralised time buffering problem (TBP) to absorb project risk by building sufficient buffers with the aim of obtaining astable project schedule. First, the position of the buffers in the project network should be determined and, subsequently, each buffer must be optimally sized. We investigate different activity clustering methods (K-means, rank order, criticality-based and network clustering) to determine the ideal groups of activities to be clustered together and protected by an allocated buffer. The obtained clusters of activities are then inputted in a multi-population multi-factorial evolutionary algorithm (MPMFEA) for creating buffers based on the characteristics of the activities in each cluster. To the best of our knowledge, this is the first study to integrate existing clustering methods into a buffering algorithm in order to optimise the project stability. Previous studies hybridising both methods use a single clustering algorithm (e.g. K-means) that does not use the same information than the buffering algorithm or require more complex (simulation- based) buffering methods. The computational experiments on a large set of artificial instances validate the effectiveness of the proposed MPMFEA for solving the TBP, especially in combination with the network clustering method. Although the generic K-Means method is still considered a viable option for clustering, the more pragmatic clustering methods are more effective. We inform project managers that considering precedence relations between activities during clustering is crucial, but mimicking this behaviour in all clustering methods does not guarantee successful protection of their projects. |
| Publikationsart: | article in journal/newspaper |
| Dateibeschreibung: | application/pdf |
| Sprache: | English |
| Relation: | https://biblio.ugent.be/publication/01JKZSMPD5KDXHD9NKR5QRBYQ0; https://doi.org/10.1016/j.cie.2024.110752; https://biblio.ugent.be/publication/01JKZSMPD5KDXHD9NKR5QRBYQ0/file/01JKZSNY5GMAFTEEWGYKK6K84D |
| DOI: | 10.1016/j.cie.2024.110752 |
| Verfügbarkeit: | https://biblio.ugent.be/publication/01JKZSMPD5KDXHD9NKR5QRBYQ0 https://hdl.handle.net/1854/LU-01JKZSMPD5KDXHD9NKR5QRBYQ0 https://doi.org/10.1016/j.cie.2024.110752 https://biblio.ugent.be/publication/01JKZSMPD5KDXHD9NKR5QRBYQ0/file/01JKZSNY5GMAFTEEWGYKK6K84D |
| Rights: | info:eu-repo/semantics/restrictedAccess |
| Dokumentencode: | edsbas.5FFA702B |
| Datenbank: | BASE |
Nájsť tento článok vo Web of Science | Abstract: | This paper studies the decentralised time buffering problem (TBP) to absorb project risk by building sufficient buffers with the aim of obtaining astable project schedule. First, the position of the buffers in the project network should be determined and, subsequently, each buffer must be optimally sized. We investigate different activity clustering methods (K-means, rank order, criticality-based and network clustering) to determine the ideal groups of activities to be clustered together and protected by an allocated buffer. The obtained clusters of activities are then inputted in a multi-population multi-factorial evolutionary algorithm (MPMFEA) for creating buffers based on the characteristics of the activities in each cluster. To the best of our knowledge, this is the first study to integrate existing clustering methods into a buffering algorithm in order to optimise the project stability. Previous studies hybridising both methods use a single clustering algorithm (e.g. K-means) that does not use the same information than the buffering algorithm or require more complex (simulation- based) buffering methods. The computational experiments on a large set of artificial instances validate the effectiveness of the proposed MPMFEA for solving the TBP, especially in combination with the network clustering method. Although the generic K-Means method is still considered a viable option for clustering, the more pragmatic clustering methods are more effective. We inform project managers that considering precedence relations between activities during clustering is crucial, but mimicking this behaviour in all clustering methods does not guarantee successful protection of their projects. |
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| DOI: | 10.1016/j.cie.2024.110752 |