## 3 An Algorithm for EDM with GA
### Construction of Emergency Decision-Making GA Based on Resource Constraints  
Emergency decision making has higher requirements for decision-making time and needs to be able to complete the decision-making process quickly and effectively. There are many decision variables involved in this paper, and the solution processes are complicated. The traditional decision method is time-consuming and laborious, and it is difficult to get an accurate answer. Genetic algorithm avoids complex solving operations and selects individuals to form new populations according to fitness function. Genetic operators are used for cross and mutation combinations to generate new solution populations. This process is called population evolution. Through the evolution from generation to generation, the new generation population has better environmental adaptability than the old generation. After many cycles of evolution, we can get some approximate optimal solutions of the problem. 1. Design of the Fitness Function  
In different regions, demands for relief workers and materials should be different at all emergency loss levels. Relief workers and materials both belong to emergency resources, and the demands of emergency resources are uncertain. Therefore, the demands can be described in the form of intervals. In this paper, the fitness function is the number of relief workers and the comprehensive evaluation value of planned dispatch materials. The number of relief workers demanded in a place \(j\) when the emergency loss level is \(i\) is denoted by \(X_{i}=[\underline{x}_{i},\overline{x}_{i}]\), where \(\underline{x}_{i}\) is the lower bound of the demand, and \(\overline{x}_{i}\) is the upper bound of the demand. \(x_{i}\) denotes the number of rescuers scheduled to be sent. This paper improves the value function of PT according to the situation of EDM, and defines the evaluation value equations of \(x_{i}\) as follows:  
\[v(x_{i})_{j}=\left\{ \begin{aligned} -\theta\left[-\left(\frac{x_{i}- \underline{x}_{i}}{\overline{x}_{i}-\underline{x}_{i}}\right)\right]^{\beta},\;x_{i}<\underline{x}_{i}\\ \left(\frac{x_{i}-\underline{x}_{i}}{\overline{x}_{i}-\underline{x }_{i}}\right)^{\alpha},\;\underline{x}_{i}\leq x_{i}\leq\overline{x}_{i}\\ 1,\;x_{i}>\overline{x}_{i}\end{aligned} \right. \tag{10}\]  
According to Equation (10), if \(x_{i}<\underline{x}_{i}\), then we say that the number of rescuers does not meet the minimum standard of emergency loss level, so the psychological expectation for the DM is negative; if \(\underline{x}_{i}\leq x_{i}\leq\overline{x}_{i}\), then we say that the decision results are in line with the prospects; if \(x_{i}>\overline{x}_{i}\), then we do not think redundant rescuers make better decisions, nor do people with diabetes increase their psychological expectations. Through Equation (10), the total value function for sending \(x_{i}\) relief workers to place \(j\) can be defined as follows:  
\[v(x)_{j}=\sum_{i=1}^{n}v(x_{i})\omega_{i}. \tag{11}\]Similarly, the number of materials demanded in a certain place when the emergency loss level is \(i\) is denoted by \(Y_{i}=[\underline{y}_{i},\overline{y}_{i}]\). The evaluation value equations of \(y_{i}\) is defined as follows:  
\[v(y_{i})_{j}=\left\{ \begin{array}{l}-\theta\Big{[}-\Big{(}\frac{y_{i}-y_{i}}{ \overline{y}_{i}-\underline{y}_{i}}\Big{)}\Big{]}^{\beta},y<\underline{y}_{i} \\ \Big{(}\frac{y_{i}-y_{i}}{\overline{y}_{i}-\underline{y}_{i}}\Big{)}^{\alpha}, \ \underline{y}_{i}\leq y_{i}\leq\overline{y}_{i}\\ 1,\ \ y_{i}>\overline{y}_{i}\end{array} \right.. \tag{12}\]  
\[v(y)_{j}=\sum_{i=1}^{n}v(y_{i})\omega_{i}. \tag{13}\]  
Combining \(v(x)\) with \(v(y)\), the objective function in this paper is the following:  
\[\max\sum_{j=1}^{m}\big{[}v(x)_{j}+v(y)_{j}\big{]}, \tag{14}\]  
where \([v(x)_{j}+v(y)_{j}]\) is the comprehensive evaluation value of decision.