A hybrid indicator many‐objective optimization algorithm for the selection and delivery of disaster relief materials problem

Abstract

Traditional methods fail to comprehensively consider the selection and delivery of disaster relief materials (SDDM) problem. To solve the problem, the SDDM will be comprehensively considered in this article, which will be built into a many-objective disaster relief materials selection and delivery model with four objectives to be optimized in the last kilometer of the disaster relief organization. Meanwhile, a many-objective optimization algorithm is designed to solve the proposed disaster relief materials model by employing the different excellent indicators. Two extensive simulation experiments are carried out. On the one hand, the performance of the designed many-objective is tested on DTLZ and WFG benchmark functions. On the other hand, the proposed many-objective model is solved by employing the designed algorithm. In addition, the simulation result shows that the designed algorithm is effective in solving the SDDMs problem. K E Y W O R D S benchmark function, indicators, many-objective optimization algorithm, selection and delivery of disaster relief materials 1 INTRODUCTION With the frequent occurrence of geological disasters in recent years, it has caused huge economic losses and human casualties such as floods, earthquakes, volcanic eruptions, forest fires. According to the laws of nature, the disasters are often irresistible and unavoidable. The planning of post-disaster relief operations will be crucial to reduce the positive damage and casualties. 1,2 Disaster relief materials often play an important role in ensuring the smooth overcoming of disasters. In other words, the timely delivery of the right relief materials to the disaster site has become extremely urgent. Therefore, there are usually two key points need to be solved for this problem from a macro perspective. On the one hand, materials with urgent rescue value should be selected for the disaster relief site. On the other hand, the selected materials should be delivered to the disaster relief site with minimum time cost. With the development of researching on this kind of problem, many excellent methods have been developed to solve the practical problem. 3,4 Wang et al 5 proposed a heuristic method based on nested partitions to solve the computational complexity problem. The basic idea of the method is to divide the solution area by fixing some variables, and determine the most promising sub-region according to the corresponding value of the linear programming relaxation problem. The simulation experiments show that the effective of this algorithm. Saeidian et al 6 compared the performance of genetic algorithm (GA) and bee algorithm (BA) in finding the optimum location of relief centers and allocating of the parcels to the distance between the center and the plot. For simulated data and real data, GA is faster than BA. Lourenco et al 7 designed an algorithm that can be executed by the software defined network (SDN) controller to generate an evacuation plan through the satellite network. The evacuation plan is the transmission plan that maximi...

A many‐objective optimization algorithm with dual criteria and mixed distribution correction strategy

“…In the framework of optimization algorithms, it is very significant to design an efficient selection strategy for improving the performance of MaOEAs. 11 According to selection strategies of MaOEAs, existing research work can be divided into four categories: the dominance-based selection method, 12 the decomposition-based selection method, 13 the indicator-based selection method, 14 and the archive-based selection method. 15 Traditional dominance-based selection strategy 16 uses Pareto dominance method to select elite solutions, and updates the non-dominant solutions according to the diversity.…”