A heuristic for improving clustering in biomass supply chains

Christou, Ioannis; Psathas, Fragkoulis; Rentizelas, Athanasios; Papadakis, Athanasios; Georgiou, Paraskevas; Anastasopoulos, Despina; Lappas, Pantelis

dc.contributor.author	Christou, Ioannis
dc.contributor.author	Psathas, Fragkoulis
dc.contributor.author	Rentizelas, Athanasios
dc.contributor.author	Papadakis, Athanasios
dc.contributor.author	Georgiou, Paraskevas
dc.contributor.author	Anastasopoulos, Despina
dc.contributor.author	Lappas, Pantelis
dc.date.accessioned	2024-07-18T07:51:52Z
dc.date.available	2024-07-18T07:51:52Z
dc.identifier.issn	2330-2674	el
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/59936
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.27632
dc.relation	https://doi.org/10.3030/101006717	el
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/gr/	*
dc.subject	Clustering	en
dc.subject	Weighted MSSC	en
dc.subject	Supply Chain	en
dc.subject	Biomass	en
dc.subject	Heuristics	en
dc.title	A heuristic for improving clustering in biomass supply chains	en
heal.type	journalArticle
heal.classification	Optimisation	en
heal.language	el
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2024-07-17
heal.bibliographicCitation	Christou, I. T., Psathas, F., Rentizelas, A., Papadakis, A., Georgiou, P. N., Anastasopoulos, D., & Lappas, P. (2024). A heuristic for improving clustering in biomass supply chains. International Journal of Systems Science: Operations & Logistics, 11(1). https://doi.org/10.1080/23302674.2024.2378859	en
heal.abstract	Clustering is commonly used in various fields such as statistics, geospatial analysis, and machine learning. In supply chain modelling, clustering is applied when the number of potential origins and/or destinations exceeds the solvable problem size. Related methods allow the reduction of the models’ dimensionality, hence facilitating their solution in acceptable timeframes for business applications. The weighted minimum sum-of-square distances clustering problem (Weighted MSSC) is a typical problem encountered in many biomass supply chain management applications, where large numbers of fields exist. This task is usually approached with the weighted K-means heuristic algorithm. This study proposes a novel, more efficient algorithm for solving the occurring weighted sum-of-squared distances minimization problem in 2-dimensional Euclidean surface. The problem is formulated as a set-partitioning problem, and a column-generation inspired approach is applied, finding better solutions than the ones obtained from the weighted version of the K-means heuristic. Results from both benchmark datasets and a biomass supply chain case show that even for large values of K, the proposed approach consistently finds better solutions than the best solutions found by other heuristic algorithms. Ultimately, this study can contribute to more efficient clustering, which can lead to more realistic outcomes in supply chain optimization.	en
heal.publisher	Taylor & Francis	en
heal.journalName	International Journal of Systems Science: Operations & Logistics	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	false
dc.identifier.doi	10.1080/23302674.2024.2378859	el