Computers and Operations Research
Fuzzy clustering with structural constraints
Fuzzy Sets and Systems
An algorithm for the capacitated, multi-commodity multi-period facility location problem
Computers and Operations Research
A heuristic method for large-scale multi-facility location problems
Computers and Operations Research
A column generation approach to capacitated p-median problems
Computers and Operations Research
Self-organizing feature maps for solving location-allocation problems with rectilinear distances
Computers and Operations Research
A simple filter-and-fan approach to the facility location problem
Computers and Operations Research - Anniversary focused issue of computers & operations research on tabu search
Bounds for the single source modular capacitated plant location problem
Computers and Operations Research
A method of face recognition based on fuzzy clustering and parallel neural networks
Signal Processing - Special section: Advances in signal processing-assisted cross-layer designs
A hybridized approach to data clustering
Expert Systems with Applications: An International Journal
Data analysis with fuzzy clustering methods
Computational Statistics & Data Analysis
Capacitated facility location problem with general setup cost
Computers and Operations Research
The capacitated centred clustering problem
Computers and Operations Research
An LP rounding algorithm for approximating uncapacitated facility location problem with penalties
Information Processing Letters
Fast accurate fuzzy clustering through data reduction
IEEE Transactions on Fuzzy Systems
A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
A novel intuitionistic fuzzy clustering method for geo-demographic analysis
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
In this study a fuzzy c-means clustering algorithm based method is proposed for solving a capacitated multi-facility location problem of known demand points which are served from capacitated supply centres. It involves the integrated use of fuzzy c-means and convex programming. In fuzzy c-means, data points are allowed to belong to several clusters with different degrees of membership. This feature is used here to split demands between supply centers. The cluster number is determined by an incremental method that starts with two and designated when capacity of each cluster is sufficient for its demand. Finally, each group of cluster and each model are solved as a single facility location problem. Then each single facility location problem given by fuzzy c-means is solved by convex programming which optimizes transportation cost is used to fine-tune the facility location. Proposed method is applied to several facility location problems from OR library (Osman & Christofides, 1994) and compared with centre of gravity and particle swarm optimization based algorithms. Numerical results of an asphalt producer's real-world data in Turkey are reported. Numerical results show that the proposed approach performs better than using original fuzzy c-means, integrated use of fuzzy c-means and center of gravity methods in terms of transportation costs.