Improved K-Means Algorithm for Capacitated Clustering Problem

The Capacitated Clustering Problem (CCP) partitions a set of n items (eg. customer orders) into k disjoint clusters with known capacity. During clustering the items with shortest assigning pathsfrom centroids are grouped together. The summation of grouped items should not exceed the capacity oFcluster. All clusters have uniform capacity. The CCP is NP-Complete and Combinatorial optimizationproblem. Combinatorial optimization problem can be viewed as searching for the best item in a set ofdiscrete items, which can be solved using search algorithm or meta heuristic. However, generic searchalgorithms have not guaranteed to find an optimal solution. Many heuristic algorithms are formulated to solve CCP. This work involves the usage of the best known clustering algorithm k-means with modification, that use priority as a measure which directs the search for better optimization. The iterativeprocedure along with priority is used for assigning the items to the clusters. This work is developedusing MATLAB 7.0.1 and tested with more than 15 problem instances of capacitated vehicle routingproblem (CVRP). The computational results are competitive when compared with the optimal solutionprovided for the problems