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Improving the quality of heuristic solutions for the capacitated vertex \(p\)-center problem through iterated greedy local search with variable neighborhood descent. (English) Zbl 1348.90412
Summary: The capacitated vertex \(p\)-center problem is a location problem that consists of placing \(p\) facilities and assigning customers to each of these facilities so as to minimize the largest distance between any customer and its assigned facility, subject to demand capacity constraints for each facility. In this work, a metaheuristic for this location problem that integrates several components such as greedy randomized construction with adaptive probabilistic sampling and iterated greedy local search with variable neighborhood descent is presented. Empirical evidence over a widely used set of benchmark data sets on location literature reveals the positive impact of each of the developed components. Furthermore, it is found empirically that the proposed heuristic outperforms the best existing heuristic for this problem in terms of solution quality, running time, and reliability on finding feasible solutions for hard instances.

90B80 Discrete location and assignment
90C27 Combinatorial optimization
90C59 Approximation methods and heuristics in mathematical programming
Full Text: DOI
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