Experimental parameter combinations.

This paper aims to solve the scheduling optimization problem in the emergency management of long-distance natural gas pipelines, with the goal of minimizing the total scheduling time. To this end, the objective function of the minimum total scheduling time is established, and the relevant constraints are set. A scheduling optimization model based on the particle swarm optimization (PSO) algorithm is proposed. In view of the high-dimensional complexity and local optimal problems, the neighborhood adaptive constrained fractional particle swarm optimization (NACFPSO) algorithm is used to solve it. The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. In addition, with the increase of pipeline complexity, NACFPSO can still maintain its advantages in convergence speed and scheduling time, especially in scheduling time, which further verifies the optimization effect of the algorithm in emergency management.本文旨在解决长距离天然气管道应急管理中的调度优化问题，以最小化总调度时长为目标。为此，本文建立了最小化总调度时长的目标函数，并设置了相关约束条件。提出了一种基于粒子群优化（Particle Swarm Optimization, PSO）算法的调度优化模型；针对高维复杂性与局部最优问题，采用邻域自适应约束分式粒子群优化（Neighborhood Adaptive Constrained Fractional Particle Swarm Optimization, NACFPSO）算法对该模型进行求解。实验结果表明，相较于传统粒子群优化算法，NACFPSO在收敛速度与调度时长两方面均表现更优：其平均收敛迭代次数为81.17次，平均调度时长为200.00分钟；而传统粒子群优化算法的平均收敛迭代次数为82.17次，平均调度时长为207.49分钟。此外，随着管道复杂度的提升，NACFPSO仍能在收敛速度与调度时长方面保持优势，尤其在调度时长维度上优势更为显著，进一步验证了该算法在应急管理场景中的优化效果。