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Author Moreno, S.; Pereira, J.; Yushimito, W.
Title (up) A hybrid K-means and integer programming method for commercial territory design: a case study in meat distribution Type
Year 2020 Publication Annals Of Operations Research Abbreviated Journal Ann. Oper. Res.
Volume 286 Issue 1-2 Pages 87-117
Keywords Territorial design; Clustering; K-means; Integer programming; Case study
Abstract The objective of territorial design for a distribution company is the definition of geographic areas that group customers. These geographic areas, usually called districts or territories, should comply with operational rules while maximizing potential sales and minimizing incurred costs. Consequently, territorial design can be seen as a clustering problem in which clients are geographically grouped according to certain criteria which usually vary according to specific objectives and requirements (e.g. costs, delivery times, workload, number of clients, etc.). In this work, we provide a novel hybrid approach for territorial design by means of combining a K-means-based approach for clustering construction with an optimization framework. The K-means approach incorporates the novelty of using tour length approximation techniques to satisfy the conditions of a pork and poultry distributor based in the region of Valparaiso in Chile. The resulting method proves to be robust in the experiments performed, and the Valparaiso case study shows significant savings when compared to the original solution used by the company.
Address [Moreno, Sebastian; Pereira, Jordi; Yushimito, Wilfredo] Univ Adolfo Ibanez, Fac Engn & Sci, Av Padre Hurtado 750, Vina Del Mar, Chile, Email: sebastian.moreno@uai.cl;
Corporate Author Thesis
Publisher Springer Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0254-5330 ISBN Medium
Area Expedition Conference
Notes WOS:000511564300005 Approved
Call Number UAI @ eduardo.moreno @ Serial 1105
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Author Alejo, L.; Atkinson, J.; Lackner, S.
Title (up) Looking deeper – exploring hidden patterns in reactor data of N-removal systems through clustering analysis Type
Year 2020 Publication Water Science and Technology Abbreviated Journal Water Sci. Technol.
Volume 81 Issue 8 Pages 1569-1577
Keywords clustering; feature selection; k-means; partial nitritation-anammox
Abstract In this work, clustering analysis of two partial nitritation-anammox (PN-A) moving bed biofilm reactors (MBBR) containing different types of carrier material was explored for the identification of patterns and operational conditions that may benefit process performance. The systems ran for two years under fluctuations of temperature and organic matter. Ex situ batch activity tests were performed every other week during the operation of these reactors. These datasets and the parameters, which were monitored online and in the laboratory, were combined and analyzed applying clustering analysis to identify non-obvious information regarding the performance of the systems. The initial results were consistent with the literature and from an operational perspective, which allowed the parameters to be explored further. The new information revealed that the oxidation reduction potential (ORP) and the anaerobic ammonium oxidizing bacteria (AnAOB) activity correlated well. ORP also dropped when the reactors were exposed to real wastewater (presence of organic matter). Moreover, operating conditions during nitrite accumulation were identified through clustering, and also revealed inhibition of anammox bacteria already at low nitrite concentrations.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0273-1223 ISBN Medium
Area Expedition Conference
Notes Approved
Call Number UAI @ eduardo.moreno @ Serial 1123
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Author Baler, R.V.; Wijnhoven, I.B.; del Valle, V.I.; Giovanetti, C.M.; Vivanco, J.F.
Title (up) Microporosity Clustering Assessment in Calcium Phosphate Bioceramic Particles Type
Year 2019 Publication Frontiers In Bioengineering And Biotechnology Abbreviated Journal Front. Bioeng. Biotechnol.
Volume 7 Issue 281 Pages 7 pp
Keywords calcium phosphate; bioceramic particle; microporosity; data mining; K-means clustering
Abstract There has been an increase in the application of different biomaterials to repair hard tissues. Within these biomaterials, calcium phosphate (CaP) bioceramics are suitable candidates, since they can be biocompatible, biodegradable, osteoinductive, and osteoconductive. Moreover, during sintering, bioceramic materials are prone to form micropores and undergo changes in their surface topographical features, which influence cellular physiology and bone ingrowth. In this study, five geometrical properties from the surface of CaP bioceramic particles and their micropores were analyzed by data mining techniques, driven by the research question: what are the geometrical properties of individual micropores in a CaP bioceramic, and how do they relate to each other? The analysis not only shows that it is feasible to determine the existence of micropore clusters, but also to quantify their geometrical properties. As a result, these CaP bioceramic particles present three groups of micropore clusters distinctive by their geometrical properties. Consequently, this new methodological clustering assessment can be applied to advance the knowledge about CaP bioceramics and their role in bone tissue engineering.
Address [Vallejos Baler, Raul; Irribarra del Valle, Victor; Vivanco, Juan F.] Adolfo Ibanez Univ, Fac Engn & Sci, Vina Del Mar, Chile, Email: juan.vivanco@uai.cl
Corporate Author Thesis
Publisher Frontiers Media Sa Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2296-4185 ISBN Medium
Area Expedition Conference
Notes WOS:000495370600001 Approved
Call Number UAI @ eduardo.moreno @ Serial 1062
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Author Heredia, C.; Moreno, S.; Yushimito, W.
Title (up) ODMeans: An R package for global and local cluster detection for Origin–Destination GPS data Type
Year 2024 Publication SoftwareX Abbreviated Journal SoftwareX
Volume 26 Issue Pages 101732
Keywords Machine learning; k-means; Odmeans; R
Abstract The ODMeans R package implements the OD-Means model, a two-layer hierarchical clustering algorithm designed for extracting both global and local travel patterns from Origin–Destination Pairs (OD-Pairs). In contrast to existing models, OD-Means automates cluster determination and offers advantages such as smaller Within-Cluster Distance (WCD) and dual hierarchies. The package includes functions for applying the model and visualizing the results on maps. Using real taxi data from Santiago, Chile, we demonstrate the package’s capabilities, showcasing its flexibility and impact on understanding urban mobility patterns.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2352-7110 ISBN Medium
Area Expedition Conference
Notes Approved
Call Number UAI @ alexi.delcanto @ Serial 1963
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