A Revised Weighted Fuzzy C-Means and Center of Gravity Algorithm for Probabilistic Demand and Customer Positions

This study proposes four probabilistic fuzzy c-means algorithms which include a probabilistic fuzzy c-means algorithm (Probabilistic FCM), a probabilistic revised weighted fuzzy c-means algorithm (Probabilistic RWFCM) and hybrid algorithms that combine these algorithms with the center of gravity met...

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Autores principales: Bayturk, Engin, Esnaf, Sakir, Kucukdeniz, Tarik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7351573/
http://dx.doi.org/10.1007/978-3-030-51156-2_177
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author Bayturk, Engin
Esnaf, Sakir
Kucukdeniz, Tarik
author_facet Bayturk, Engin
Esnaf, Sakir
Kucukdeniz, Tarik
author_sort Bayturk, Engin
collection PubMed
description This study proposes four probabilistic fuzzy c-means algorithms which include a probabilistic fuzzy c-means algorithm (Probabilistic FCM), a probabilistic revised weighted fuzzy c-means algorithm (Probabilistic RWFCM) and hybrid algorithms that combine these algorithms with the center of gravity methods for the un-capacitated planar multi-facility location problem when customer positions and customer demands are probabilistic with predetermined service level. The performance of proposed algorithms was tested with 13 data sets and compared with each other. Experimental results indicate that Probabilistic RWFCM-COG algorithm performs better than other compared algorithms in terms of cost minimization.
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spelling pubmed-73515732020-07-13 A Revised Weighted Fuzzy C-Means and Center of Gravity Algorithm for Probabilistic Demand and Customer Positions Bayturk, Engin Esnaf, Sakir Kucukdeniz, Tarik Intelligent and Fuzzy Techniques: Smart and Innovative Solutions Article This study proposes four probabilistic fuzzy c-means algorithms which include a probabilistic fuzzy c-means algorithm (Probabilistic FCM), a probabilistic revised weighted fuzzy c-means algorithm (Probabilistic RWFCM) and hybrid algorithms that combine these algorithms with the center of gravity methods for the un-capacitated planar multi-facility location problem when customer positions and customer demands are probabilistic with predetermined service level. The performance of proposed algorithms was tested with 13 data sets and compared with each other. Experimental results indicate that Probabilistic RWFCM-COG algorithm performs better than other compared algorithms in terms of cost minimization. 2020-06-10 /pmc/articles/PMC7351573/ http://dx.doi.org/10.1007/978-3-030-51156-2_177 Text en © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Bayturk, Engin
Esnaf, Sakir
Kucukdeniz, Tarik
A Revised Weighted Fuzzy C-Means and Center of Gravity Algorithm for Probabilistic Demand and Customer Positions
title A Revised Weighted Fuzzy C-Means and Center of Gravity Algorithm for Probabilistic Demand and Customer Positions
title_full A Revised Weighted Fuzzy C-Means and Center of Gravity Algorithm for Probabilistic Demand and Customer Positions
title_fullStr A Revised Weighted Fuzzy C-Means and Center of Gravity Algorithm for Probabilistic Demand and Customer Positions
title_full_unstemmed A Revised Weighted Fuzzy C-Means and Center of Gravity Algorithm for Probabilistic Demand and Customer Positions
title_short A Revised Weighted Fuzzy C-Means and Center of Gravity Algorithm for Probabilistic Demand and Customer Positions
title_sort revised weighted fuzzy c-means and center of gravity algorithm for probabilistic demand and customer positions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7351573/
http://dx.doi.org/10.1007/978-3-030-51156-2_177
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