Temporal Relief using Fractal Search

After disasters, preparation for performing an effective rescue mission can significantly reduce the costs and possible injuries of the event. Relief is regarded as one of the crucial steps in developing of disaster management systems. This research has tackled a multi-product multi-period inventory routing problem in order to develop an efficient strategy for temporal relief tasks. To solve the model, an improved fractal search algorithm was employed. Fractal search is a population-based and powerful optimization algorithm that explores the problem space based on the potential theory, growth of the random fractal and three physical laws. Based on instance problems, the efficiency of the proposed algorithm is compared to other methods in terms of running time, convergence rate, success rate, best and average standard deviation of the results, as well as the statistical superiority by test of Wilcoxon. Assessment of the results show that the proposed algorithm has a better performance based on running time, convergence speed, and success rate in temporal routing of rescue vehicles. The results show that by increasing of problem dimensions, better efficiency in the proposed strategy is detected. The proposed framework in this study can be used in relief scenarios and related inventory routing problems.