A Biased Random-Key Genetic Algorithm for the Rescue Unit Allocation and Scheduling Problem

CUNHA, VICTOR ABU-MARRUL CARNEIRO DA; Pessôa, Luciana Fontes; Vellasco, Marley; Tanscheit, Ricardo; Pacheco, Marco Aurélio Cavalcanti

doi:10.1109/cec.2018.8477819

Cited by 8 publications

(2 citation statements)

References 15 publications

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“…They also provided NSGA-II and C-METRIC approaches to solve the problem and developed scheduling strategies for a specific disaster situation. Cunha et al [46] developed a biased random-key genetic algorithm for allocation and scheduling of the relief teams in the natural disaster. They considered fuzzy processing times for the incidents and showed the proposed algorithm could obtain high-quality solutions.…”

Section: Literature Reviewmentioning

confidence: 99%

Designing a bi-objective decision support model for the disaster management

et al. 2021

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This paper addresses the allocation and scheduling of the relief teams as one of the main issues in the response phase of the disaster management. In this study, a Bi-Objective Mixed Integer Programming (BOMIP) model is proposed to assign and schedule of the relief teams in the disasters. The first objective function aims to minimize the sum of weighted completion times of the incidents and the second objective function also minimizes the sum of weighted tardiness of the relief operations. In order to more similar to the real-world, time-windows for the incidents and damaged routes are considered in this research. Furthermore, the actual relief time of an incident by the relief team is calculated according to the position on the corresponding relief team and fatigue effect. Due to NP-hardness of the considered problem, the proposed model cannot present the optimal solution in the reasonable time. Thus, NSGA-II and PSO algorithms are applied to solve the problem. Furthermore, the obtained results of the proposed algorithms are compared with respect to different performance metrics in large size test problems. Finally, in order to investigate the impact of some parameters on the Pareto frontier, the sensitivity analysis and the managerial suggestions are provided.

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Section: Literature Reviewmentioning

confidence: 99%

Designing a bi-objective decision support model for the disaster management

et al. 2021

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“…GAs are optimization algorithms that can estimate many parameters to find the optimal value of an objective function [15]. Such algorithms are also known for their robustness in solving nonlinear problems [16,17]. In research on earthquake problems, GAs have been used to understand focal mechanisms and stress tensor inversion [18,19], carry out InSAR data inversion to learn about deformation [20], predict earthquakes [21], and select earthquake ground motion [22].…”

Section: Introductionmentioning

confidence: 99%

Rapid Estimation of Earthquake Magnitude and Source Parameters Using Genetic Algorithms

et al. 2021

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To minimize the impacts of large losses and optimize the emergency response when a large earthquake occurs, an accurate early warning of an earthquake or tsunami is crucial. One important parameter that can provide an accurate early warning is the earthquake’s magnitude. This study proposes a method for estimating the magnitude, and some of the source parameters, of an earthquake using genetic algorithms (GAs). In this study, GAs were used to perform an inversion of Okada’s model from earthquake displacement data. In the first stage of the experiment, the GA was used to inverse the displacement calculated from the forward calculation in Okada’s model. The best performance of the GA was obtained by tuning the hyperparameters to obtain the most functional configuration. In the second stage, the inversion method was tested on GPS time series data from the 2011 Tohoku Oki earthquake. The earthquake’s displacement was first estimated from GPS time series data using a detection and estimation formula from previous research to calculate the permanent displacement value. The proposed method can estimate an earthquake’s magnitude and four source parameters (i.e., length, width, rake, and slip) close to the real values with reasonable accuracy.

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