A genetic algorithm based approach for emergency material dispatch among multiple disaster points using the principle of balance

作者：Zhu Ling^*; Wang Yan; Meng X H; Zhang W Y

来源：International Journal of Digital Content Technology and Its Applications, 2011, 5(9): 112-124.

DOI：10.4156/jdcta.vol5.issue9.13

• Candidate materials
• e-Government
• Emergency material
• Fitness functions
• Global optimal solutions
• Multi objective
• Multiple disaster points
• Optimal solutions
• Semantic similarity
• The principle of balance

In e-government, emergency material dispatch is one of hot issues attracting concerns from both the government and the public. When there are multiple disaster points, the problem of uneven distribution and competition of resources has frequently risen, to make it hard to find a global optimal solution. A comprehensive emergency material dispatch model for multiple disaster points, by dealing with the trade-offs among many indices, such as time, the amount of resources, the number of rescue points, etc. is put forward in this paper. Considering the real significance of similarity between the demanding material and the candidate material in the emergency dispatch, the semantic similarity is in particular taken into account as an important index in the multi-objective decision-making mathematical model. In addition, genetic algorithm (GA) is adopted to search the optimal solution for the model. Accordingly, a genetic algorithm based approach for the emergency material dispatch among multiple disaster points using the principle of balance is proposed in order to acquire the global optimal solution. A matrix-based approach for calculating fitness function of every individual is introduced to make the solution more reasonable. This method aims to make the dispatch process of emergency material both timely and fair to ensure early implementation of disaster emergency work in some extents. A case study is carried out to analyze and verify the effectiveness of the approach demonstrated in this paper.

• 出版日期2011
• 单位浙江大学