2022
DOI: 10.1016/j.cor.2022.105804
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A Greedy Randomized Adaptive Search Procedure (GRASP) for the multi-vehicle prize collecting arc routing for connectivity problem

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Cited by 13 publications
(9 citation statements)
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References 26 publications
(43 reference statements)
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“…This Section explains the procedure to create the pre-disaster network, following an approach inspired by Souza Almeida et al. [8] , which is summarized in Fig. 7 .…”
Section: Experimental Design Materials and Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…This Section explains the procedure to create the pre-disaster network, following an approach inspired by Souza Almeida et al. [8] , which is summarized in Fig. 7 .…”
Section: Experimental Design Materials and Methodsmentioning
confidence: 99%
“…The disaster intensity is specified with “M” for medium impact and “H” for high impact. When the disaster occurs, it is assumed that the initial network is subdivided into multiple disconnected subnetworks called “components”, as defined in the previous studies of KPC-ARCP [6] , [7] , [8] . The second number in the instance name refers to the exact number of components of the damaged network.…”
Section: Data Descriptionmentioning
confidence: 99%
“…e time unit is ms. From the comparison test results of search-based methods, it can be seen that the self-adaptive decision method proposed in this paper and the composite decision method proposed by [27], with the increase in damage ratio, the time-consuming of the decision process is also increasing, but neither of them is more than 2 s. It is obvious that the decision process of parallel method is generally less timeconsuming than that of the serial method. In most cases, the time consumption of [28] the decision method is generally lower than that of serial and parallel methods.…”
Section: Comparative Experimental Testsmentioning
confidence: 99%
“…[26][27][28] GRASP is a potent metaheuristic that is effectively used to solve several optimization problems. [29][30][31][32][33][34] GRASP is empowered by the combination of the following advantages: (a) greediness to generate reasonably good initial solutions, (b) randomization to explore different solution spaces, (c) local search to find local optima, and (d) multistart to select the best local optima. Furthermore, GRASP provides the following appealing characteristics, which introduce it as a promising technique to solve the task scheduling problem: First, it is simple and can easily be implemented in real-world scenarios.…”
Section: Introductionmentioning
confidence: 99%