Scale of test cases.

Efficient operation of natural gas pipeline networks is essential for minimizing costs and ensuring a stable energy supply. Compressor stations are critical for maintaining pressure throughout the network and represent a substantial portion of both capital investment and operational expenditures. Optimizing compressor operations is complex due to nonlinear interactions among compression ratios, flow rates, and operational constraints such as surge and choke limits, as well as the need for discrete decisions, including bypass selection and flow direction. To address these challenges, this study introduces a Sequential Linear Programming (SLP) algorithm specifically designed for large-scale pipeline scheduling. The method accommodates compressor capacity constraints and discrete decision variables, resulting in significant improvements in computational efficiency and solution quality. Numerical experiments on extensive real-world pipeline networks in China demonstrate that the algorithm rapidly produces near-optimal solutions, indicating its suitability for practical implementation. The results underscore clear advantages in both economic performance and operational reliability.