Multi-objective quantum genetic algorithm for site selection optimization of emergency material reserves

Emergency material reserves are essential infrastructure for ensuring the efficiency and responsiveness of emergency rescue operations. To address the location optimization problem of emergency supply depots, this study develops a multi-objective optimization model that aims to minimize road network transportation distance and disaster risk, thereby enhancing the efficiency and reliability of emergency rescue. To overcome the limitations of traditional intelligent optimization algorithms, dueto the problem of easily falling into local optima, making it difficult to approximate the Pareto front, in multi-objective optimization of road network transportation distance and disaster risk, this study proposes a multi-objective quantum non-dominated sorting genetic algorithm (MOQNSGA). The algorithm enhances solution diversity through the quantum bit entanglement mechanism and employs adaptive rotation gates to efficiently update individual quantum states, thereby improving the global search capability in the solution space. Taking 16 administrative districts of Beijing as the research case, the algorithm's performance in optimizing road network transportation distance and disaster risk management is evaluated and compared against traditional multi-objective optimization algorithms. In ten independent experiments, the algorithm achieved an average coverage rate of 77.41% for the Pareto front solution set. On the Pareto front, the minimum values of transportation distance in the road network and disaster risk reached 357.40 and 478.47, respectively. Compared with the traditional non-dominated sorting genetic algorithm II, the multi-objective particle swarm optimization algorithm, and the multi-objective rime optimization algorithm, the proportions of Pareto front solutions increased by 61.75%, 70.48%, and 77.41%, respectively, and the proportion of Pareto front solution sets was significantly better than that of the comparison algorithm, showing obvious advantages in solution quality and diversity. The algorithm outperforms conventional methods in terms of optimizing road network transportation distance, disaster risk management, rationality of route planning, and overall layout balance, indicating that the multi-objective quantum non-dominated sorting genetic algorithm has notable advantages and practical application value in optimizing the location selection for emergency material reserve depots.