A Multi-Objective Parallel Iterated Greedy for Solving the p-Center and p-Dispersion Problem

Pérez‐Peló, Sergio; Sánchez‐Oro, Jesús; López-Sánchez, Ana Dolores; Duarte, Abraham

doi:10.3390/electronics8121440

Cited by 9 publications

(5 citation statements)

References 40 publications

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“…This problem arises in the context of facility location problems, where the set of selected elements refers to the facilities that would be opened, while the set of non-selected ones represents demand points that are required from the services of the opened facilities. This problem has been addressed by using several metaheuristic approaches, iterated greedy [35] and VNS [36] being the most recent ones.…”

Section: Mmd(s) = Minmentioning

confidence: 99%

Multi-Objective GRASP for Maximizing Diversity

et al. 2021

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This work presents a novel greedy randomized adaptive search procedure approach for dealing with the maximum diversity problem from a multi-objective perspective. In particular, five of the most extended diversity metrics were considered, with the aim of maximizing all of them simultaneously. The metrics considered have been proven to be in conflict, i.e., it is not possible to optimize one metric without deteriorating another one. Therefore, this results in a multi-objective optimization problem where a set of efficient solutions that are diverse with respect to all the metrics at the same time must be obtained. A novel adaptation of the well-known greedy randomized adaptive search procedure, which has been traditionally used for single-objective optimization, was proposed. Two new constructive procedures are presented to generate a set of efficient solutions. Then, the improvement phase of the proposed algorithm consists of a new efficient local search procedure based on an exchange neighborhood structure that follows a first improvement approach. An effective exploration of the exchange neighborhood structure is also presented, to firstly explore the most promising ones. This feature allowed the local search proposed to limit the size of the neighborhood explored, resulting in an efficient exploration of the solution space. The computational experiments showed the merit of the proposed algorithm, when comparing the obtained results with the best previous method in the literature. Additionally, new multi-objective evolutionary algorithms derived from the state-of-the-art were also included in the comparison, to prove the quality of the proposal. Furthermore, the differences found were supported by non-parametric statistical tests.

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Section: Mmd(s) = Minmentioning

confidence: 99%

Multi-Objective GRASP for Maximizing Diversity

et al. 2021

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“…In 2019, Tutunchi and Fathi [40] and Pérez-Peló et al [41] took into account two objectives: the minimization of the maximum distance among facilities and demand points (the k-Center problem), and the maximization of the minimum distance between all pairs of facilities (k-Dispersion problem).…”

Section: Literature Reviewmentioning

confidence: 99%

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

et al. 2021

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This paper presents a hybridization of Strategic Oscillation with Path Relinking to provide a set of high-quality nondominated solutions for the Multiobjective k-Balanced Center Location problem. The considered location problem seeks to locate k out of m facilities in order to serve n demand points, minimizing the maximum distance between any demand point and its closest facility while balancing the workload among the facilities. An extensive computational experimentation is carried out to compare the performance of our proposal, including the best method found in the state-of-the-art as well as traditional multiobjective evolutionary algorithms.

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“…In [12], the authors presented the stochastic gradual cover model in which they assumed that the short and long distances employed in gradual cover models are random variables. In [13] authors used generalized iterative greedy algorithm to solve a multiobjective facility location problem. Cattani et al [14], Mak and Shen [15], and Berman and Kras [16] considered stochastic demand.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

A Novel Approach for Determination of Reliability of Covering a Node from K Nodes

Panić

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Vujošević

et al. 2020

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In this paper, a stochastic problem of multicenter location on a graph was formulated through the modification of the existing p-center problem to determine the location of a given number of facilities, to maximize the reliability of supplying the system. The system is represented by a graph whose nodes are the locations of demand and the potential facilities, while the weights of the arcs represent the reliability, i.e., the probability that an appropriate branch is available. First, k locations of facilities are randomly determined. Using a modified Dijkstra’s algorithm, the elementary path of maximal reliability for every demand node is determined. Then, a graph of all of elementary paths for demand node is formed. Finally, a new algorithm for calculating the reliability of covering a node from k nodes (k—covering reliability) was formulated.

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A Multi-Objective Parallel Iterated Greedy for Solving the p-Center and p-Dispersion Problem

Cited by 9 publications

References 40 publications

Multi-Objective GRASP for Maximizing Diversity

Multi-Objective GRASP for Maximizing Diversity

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

A Novel Approach for Determination of Reliability of Covering a Node from K Nodes

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