Emergency Logistics Operation Strategy Based on Improved TOPSIS and Two-Stage Heuristic Algorithm

With the frequent occurrence of natural disasters, emergency logistics operation decisions are directly related to the efficiency of post-disaster rescue. However, traditional logistics operation strategy methods often overlook the particularity of disaster environments and cannot meet the actual needs of emergency logistics. Therefore, this study proposes an emergency logistics operation strategy based on an improved technique for order preferences by similarity to an ideal solution (TOPSIS) and a two-stage heuristic algorithm to meet practical demands. First of all, the TOPSIS method is used to sort and select the good and bad schemes, and deal with multi-objective and multi-constraint problems, including minimizing response time, reducing total costs, optimizing resource allocation fairness, while constraints may involve material supply capacity, transportation time limits, and service quality requirements. Second, a two-stage heuristic algorithm is utilized to find the optimal solution in complex decision-making environments. It is aimed at improving the efficiency of emergency logistics operations and addressing the complexity and uncertainty of emergency logistics operation strategies. This method can not only be combined with traditional cost optimization methods, but also flexibly integrated into emergency response, with high compatibility. The experimental results showed that as the service level of the distribution center improved, the response time of the center was shortened from 11.18 s to 12.58 s, with an average of 11.88 s. The total system cost was also reduced from 8.73 to 5.64, with an average of 6.58. This indicates that this method improves the scientificity and accuracy of emergency logistics operations, providing scientific decision-making basis for dealing with various possible disasters in the future.