Scatter search for the bi-criteria p-median p-dispersion problem

A Reactive Path Relinking algorithm for solving the Bi-objective p-Median and p-Dispersion problem

“…The Scatter Search (SS) procedure reported in Colmenar et al (2018) is another evolutionary algorithm that starts by randomly constructing a set of solutions to generate the initial population. Then, they are improved with three different local search methods, which are based on the first improvement strategy.…”

“…The algorithms providing the best results for the BpMD problem in the literature were proposed by Sayyady et al (2015) and Colmenar et al (2018), therefore, both algorithms have been considered in the competitive testing of Section 4. Furthermore, the comparison includes the well-known multi-objective evolutionary algorithms: MOEA/D, Multi-Objective Evolutionary Algorithm based on Decomposition (Zhang & Li, 2007); NSGA-II, elitist Non-dominated Sorting Genetic Algorithm (Deb et al, 2002);and SPEA2, Strength Pareto Evolutionary Algorithm (Zitzler et al, 2001).…”

The enriched median routing problem and its usefulness in practice

“…In the Bi-Criteria p-Median p-Dispersion Problem, we not only minimize the summed distances of nodes to their medians, but we also maximize the smallest distance between any two medians. Colmenar, Hoff, Martí, and Duarte (2018) propose a Scatter Search matheuristic to find solutions on or near the Pareto front of these two objectives.…”

A Hybrid Strategic Oscillation with Path Relinking Algorithm for the Multiobjective k-Balanced Center Location Problem

“…Wang et al [36] considers the maximization of the total coverage provided by the selected facilities (the maximal coverage location problem) and, simultaneously, the minimization of the total distance (k-Median problem). The authors in Colmenar et al [37] minimized the total distance among demand points and facilities (k-Median problem), and maximized the minimum distance between the selected facilities (k-Dispersion problem); Daskin and Tucker [33] solved a biobjective optimization problem by minimizing the total distance between each demand point and its closest facility (k-Median problem) and balancing the facility workload measured as the difference between the maximum and the minimum demand assigned to any facility. The same year, Karatas and Yakıcı [38] first proposed another multiobjective FLP in which three objectives are considered: the minimization of the maximum distance among demand points and facilities (k-Center problem), the minimization of the total distance among demand points and facilities (k-Median problem), and the maximization of the coverage provided by the selected facilities to demand points (the maximal coverage location problem); this same problem was also solved by López-Sánchez et al [39] in 2020.…”