Abstract
References
- Askari, S. (2021) “Fuzzy C-Means Clustering Algorithm for Data With Unequal Cluster Sizes and Contaminated With Noise and Outliers: Review and Development”, Expert Systems with Applications, 165. Azem, Z. (2013) “A Comprehensive Cluster Validity Framework for Clustering Algorithms”, MSc Thesis, The University of Guelph, Canada.
- Basti, M. (2012) “P-medyan Tesis Yeri Seçim Problemi ve Çözüm yaklaşımları”, AJIT-e: Academic Journal of Information Technology, 3(7): 47-75.
- Basti, M. and Sevkli, M. (2015) “An Artificial Bee Colony Algorithm for The P-median Facility Location Problem”, International Journal of Metaheuristics, 4(1): 91-113.
- Bezdek J.C. (1981) “Pattern Recognition with Fuzzy Objective Function Algorithms”, NY: Plenum Press.
- Birgün, S. and Ulu, M. (2021) “Site Selection for a Training Centre Focused on Industry 4.0 by Using Dematel and Copras”, In Digital Conversion on the Way to Industry 4.0: Selected Papers from ISPR2020, Springer International Publishing.
- De Oliveira, J. V. and Pedrycz, W. (2007) “Advances in Fuzzy Clustering and Its Applications”, John Wiley and Sons.
- Dunn, J.C. (1974) “A Fuzzy Relative Isodata Process and its Use in Detecting Compact Well-Separated Clusters”, Journal of Cybern, 3: 32-57.
- Durak, İ. and Yıldız, M. (2015) “P-Medyan Tesis Yeri Seçim Problemi: Bir Uygulama”, Uluslararası Alanya İşletme Fakültesi Dergisi, 7(2): 43-64.
- Esnaf, Ş. and Küçükdeniz, T. (2009) “A Fuzzy Clustering-Based Hybrid Method For A Multi-Facility Location Problem”, Journal of Intelligent Manufacturing, 20: 259-265.
- Esnaf, Ş. and Küçükdeniz, T. (2013) “Solving Uncapacitated Planar Multi-facility Location Problems by a Revised Weighted Fuzzy C-Means Clustering Algorithm”, Journal of Multiple-Valued Logic & Soft Computing, 21.
Abstract
Choosing the right warehouse location reduces costs while increasing efficiency and customer satisfaction in logistics processes. However, the choice of warehouse location usually involves a large number of uncertain factors. This study examines the fuzzy c-means method in the warehouse location selection process. Using the principles of fuzzy logic, it offers a methodology that allows the warehouse location to be evaluated with uncertainty and imprecise data. The flexibility, uncertainty, and successful applicability of fuzzy logic to real-world problems are important in decision-making processes such as warehouse location. The fuzzy C-means method is a clustering algorithm used to identify groups (clusters) in the data set. This approach makes decisions regarding warehouse location selection more accurate and supported by information. The results of the study show that the fuzzy C-means method can be used effectively in warehouse location selection and that this approach adds value to the decision processes in logistics management. This methodology can be used in decision-making processes on logistics planning and strategic selection of warehouse locations, while helping businesses increase their competitive advantage.
References
- Askari, S. (2021) “Fuzzy C-Means Clustering Algorithm for Data With Unequal Cluster Sizes and Contaminated With Noise and Outliers: Review and Development”, Expert Systems with Applications, 165. Azem, Z. (2013) “A Comprehensive Cluster Validity Framework for Clustering Algorithms”, MSc Thesis, The University of Guelph, Canada.
- Basti, M. (2012) “P-medyan Tesis Yeri Seçim Problemi ve Çözüm yaklaşımları”, AJIT-e: Academic Journal of Information Technology, 3(7): 47-75.
- Basti, M. and Sevkli, M. (2015) “An Artificial Bee Colony Algorithm for The P-median Facility Location Problem”, International Journal of Metaheuristics, 4(1): 91-113.
- Bezdek J.C. (1981) “Pattern Recognition with Fuzzy Objective Function Algorithms”, NY: Plenum Press.
- Birgün, S. and Ulu, M. (2021) “Site Selection for a Training Centre Focused on Industry 4.0 by Using Dematel and Copras”, In Digital Conversion on the Way to Industry 4.0: Selected Papers from ISPR2020, Springer International Publishing.
- De Oliveira, J. V. and Pedrycz, W. (2007) “Advances in Fuzzy Clustering and Its Applications”, John Wiley and Sons.
- Dunn, J.C. (1974) “A Fuzzy Relative Isodata Process and its Use in Detecting Compact Well-Separated Clusters”, Journal of Cybern, 3: 32-57.
- Durak, İ. and Yıldız, M. (2015) “P-Medyan Tesis Yeri Seçim Problemi: Bir Uygulama”, Uluslararası Alanya İşletme Fakültesi Dergisi, 7(2): 43-64.
- Esnaf, Ş. and Küçükdeniz, T. (2009) “A Fuzzy Clustering-Based Hybrid Method For A Multi-Facility Location Problem”, Journal of Intelligent Manufacturing, 20: 259-265.
- Esnaf, Ş. and Küçükdeniz, T. (2013) “Solving Uncapacitated Planar Multi-facility Location Problems by a Revised Weighted Fuzzy C-Means Clustering Algorithm”, Journal of Multiple-Valued Logic & Soft Computing, 21.
Details
| Primary Language | English |
|---|---|
| Subjects | Finance |
| Journal Section | Articles |
| Authors | |
| Early Pub Date | December 25, 2023 |
| Publication Date | January 1, 2024 |
| Published in Issue | Year 2023 Volume: 21 Issue: 4 |