A Graph Clustering Based Decomposition Approach for Large Scale p-Median Problems
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.
Disclaimer/Regarding indexing issue:
We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.thentic information.