A Biased Random Key Genetic Algorithm for Solving the  \(\alpha \) -Neighbor p-Center Problem

In this paper, a Biased Random Key Genetic Algorithm is proposed to solve the \(\alpha \) -neighbor p-center problem. A decoder and a local search procedure are developed obtaining competitive solutions for the problem. The objective of the ANPC is to locate p facilities serving demand points and assign a number \(\alpha \) of facilities to each demand point. The objective function is evaluated as the maximum distance to the farthest facility assigned to each client, and the goal is to minimize this maximum distance. The proposed algorithm is compared with the best method found in the literature. The performance of the algorithm is evaluated over a large set of instances showing the robustness of the proposal.