Scheduling optimization based on particle swarm optimization algorithm in emergency management of long-distance natural gas pipelines

Huichao Guo, Runhua Huang and Shuqin Cheng

PLOS ONE, 2025, vol. 20, issue 2, 1-17

Abstract: 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.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0317737

DOI: 10.1371/journal.pone.0317737

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