Advanced Modeling and Algorithm for Pipeline Scheduling

In practice, the scheduling of gas and oil pipeline networks is a continuous and dynamic process with a large number of uncertainties. Therefore, shorter calculation time and solution quality are always important indicators to measure the practicability of solution methods, especially in the real-time and active pipeline scheduling. The emergence of digital and intelligent technologies makes the data sensing and decision-making possible to be real-time and continuous. This chapter summarizes existing research from the perspective of modeling methods and solution algorithms, so as to provide the research basis and research direction for intelligent scheduling of pipeline networks. The modeling methods are mainly divided into mathematical programming, generalized disjunctive programming (GDP), and resource task network (RTN). The solution algorithms include mathematical programming, heuristic algorithm, metaheuristic algorithm, dynamic programming algorithm, and data-driven algorithm.