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A Graph Clustering Based Decomposition Approach for Large Scale p-Median Problems

Adriano Masone, Antonio Sforza, Claudio Sterle, Igor Vasiliev


The p-median problem (PMP) is the well known network optimization problem of discrete location theory. In many real applications PMPs is defined on very large scale networks, for
which ad-hoc exact and/or heuristic methods have to be developed. To this aim, in this work we propose a heuristic decomposition approach which exploits the decomposition of the
network into disconnected components obtained by a graph clustering algorithm. Then, in each component several PMPs are solved for suitable ranges of p by a Lagrangian dual and
simulated annealing based algorithm. The solution of the whole initial problem is obtained combining all the PMPs solutions through a multi-choice knapsack model. The proposed
approach is tested using several graph clustering algorithms and compared with the results of the state-of-the-art heuristic methods.


large scale p-median, graph clustering/partitioning.

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