Simulated annealing - an annotated bibliography
American Journal of Mathematical and Management Sciences
Introduction to algorithms
Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Modern heuristic techniques for combinatorial problems
Cluster analysis and mathematical programming
Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
Data clustering analysis in a multidimensional space
Information Sciences: an International Journal
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extending the Kohonen self-organizing map networks for clustering analysis
Computational Statistics & Data Analysis
Clustering Algorithms
Tabu Search
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Simultaneous grouping of parts and machines with an integrated fuzzy clustering method
Fuzzy Sets and Systems
A column generation approach to capacitated p-median problems
Computers and Operations Research
Constructive Genetic Algorithm for Clustering Problems
Evolutionary Computation
Cluster Analysis
Clustering search algorithm for the capacitated centered clustering problem
Computers and Operations Research
| Hi-index | 0.01 |
The capacitated centred clustering problem (CCCP) consists of defining a set of clusters with limited capacity and maximum proper similarity per cluster. Each cluster is composed of individuals from whom we can compute a centre value and hence, determine a similarity measure. The clusters must cover the demands of their individuals. This problem can be applied to the design of garbage collection zones, defining salesmen areas, etc. In this work, we present two variations (p-CCCP and Generic CCCP) of this problem and their mathematical programming formulations. We first focus our attention on the Generic CCCP, and then we create a new procedure for p-CCCP. These problems being NP-HARD, we propose a two-phase polynomial heuristic algorithm. The first phase is a constructive phase for which we will propose two variants: the first technique uses known cluster procedures oriented by a log-polynomial geometric tree search, the other one uses unconstrained to constrained clustering. The second phase is a refinement of the variable neighbourhood search (VNS). We also show the results we have obtained for tests from the CCP literature, the design of garbage collection zones, and salesmen areas distribution using the approach implemented for the SISROT® system.