A Greedy Randomized Adaptive Search Procedure (GRASP) for the multi-vehicle prize collecting arc routing for connectivity problem

Almeida, Luana Souza; Goerlandt, Floris; Pelot, Ronald; Sörensen, Kenneth

doi:10.1016/j.cor.2022.105804

Cited by 13 publications

(9 citation statements)

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%

“…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%

See 1 more Smart Citation

Benchmark instances for road network repair and restoration problems in the context of disaster response operations

Almeida

Kodali²,

Goerlandt³

2022

Data in Brief

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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%

Section: Data Descriptionmentioning

confidence: 99%

Benchmark instances for road network repair and restoration problems in the context of disaster response operations

Almeida

Kodali²,

Goerlandt³

2022

Data in Brief

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“…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%

Adaptive Decision Method in C3I System

Gao

Wang

Nazarko

et al. 2022

Computational Intelligence and Neuroscience

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As an important system in the national defense and military information construction, the command, control, communication, and intelligence (C3I) system urgently needs to establish an adaptive process to deal with the dynamic operating environment and changeable task requirements to ensure the long-term effective and stable operation of the system. As an important part of this process, the adaptive decision method should have the ability of online trade-off decision. Therefore, this paper presents an adaptive decision method based on parallel computing and optimization theory. This method combines operational requirements and commander preference to achieve the parallel adaptive decision solution. The experimental results show that the presented decision method can generate online trade-off strategies to deal with typical command and control scenarios of damage replacement in a simulated environment, effectively guide the system to carry out adjustment behavior, and achieve the goal of dynamic response to environmental changes and task changes.

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“…[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%

A greedy randomized adaptive search procedure for scheduling IoT tasks in virtualized fog–cloud computing

Salimi,

Azizi,

Abawajy

2024

Trans Emerging Tel Tech

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Virtualized fog–cloud computing (VFCC) has emerged as an optimal platform for processing the increasing number of emerging Internet of Things (IoT) applications. VFCC resources are provisioned to IoT applications in the form of virtual machines (VMs). Effectively utilizing VMs for diverse IoT tasks with varying requirements poses a significant challenge due to their heterogeneity in processing power, communication delay, and energy consumption. In addressing this challenge, in this article, we propose a system model for scheduling IoT tasks in VFCCs, considering not only individual task deadlines but also the system's overall energy consumption. Subsequently, we employ a greedy randomized adaptive search procedure (GRASP) to determine the optimal assignment of IoT tasks among VMs. GRASP, a metaheuristic‐based technique, offers appealing characteristics, including simplicity, ease of implementation, a limited number of tuning parameters, and the potential for parallel implementation. Our comprehensive experiments evaluate the effectiveness of the proposed method, comparing its performance with the most advanced algorithms. The results demonstrate that the proposed approach outperforms the existing methods in terms of deadline satisfaction ratio, average response time, energy consumption, and makespan.

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A Greedy Randomized Adaptive Search Procedure (GRASP) for the multi-vehicle prize collecting arc routing for connectivity problem

Cited by 13 publications

References 26 publications

Benchmark instances for road network repair and restoration problems in the context of disaster response operations

Benchmark instances for road network repair and restoration problems in the context of disaster response operations

Adaptive Decision Method in C3I System

A greedy randomized adaptive search procedure for scheduling IoT tasks in virtualized fog–cloud computing

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