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- W2980493098 abstract "We introduce the conditional p-dispersion problem (c-pDP), an incremental variant of the p-dispersion problem (pDP). In the c-pDP, one is given a set N of n points, a symmetric dissimilarity matrix D of dimensions $$ntimes n$$ , an integer $$pge 1$$ and a set $$Qsubseteq N$$ of cardinality $$qge 1$$ . The objective is to select a set $$Psubset Nsetminus Q$$ of cardinality p that maximizes the minimal dissimilarity between every pair of selected vertices, i.e., $$z(Pcup Q) {:}{=}min {D(i, j), i, jin Pcup Q}$$ . The set Q may model a predefined subset of preferences or hard location constraints in incremental network design. We adapt the state-of-the-art algorithm for the pDP to the c-pDP and include an ad-hoc acceleration mechanism designed to leverage the information provided by the set Q to further reduce the size of the problem instance. We perform exhaustive computational experiments and show that the proposed acceleration mechanism helps reduce the total computational time by a factor of five on average. We also assess the scalability of the algorithm and derive sensitivity analyses." @default.
- W2980493098 created "2019-10-25" @default.
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- W2980493098 date "2019-08-01" @default.
- W2980493098 modified "2023-10-16" @default.
- W2980493098 title "The conditional (p)-dispersion problem" @default.
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