Abstract
ABSTRACTA root cause is the original source of many systematic problems. This article studies the properties of root causes, and their roles in system optimization. Specifically, this article focuses on the train timetable optimization problem, and discusses the root causes in two scenarios: (1) timetable defects in a rail network, and (2) delays in a metro line. In each scenario, a state graph is introduced, and a mixed integer programming model is proposed to formulate the optimization problem. The solution algorithm is designed to deal with the root causes, based on a critical path algorithm. Numerical examples show that the solution algorithms can effectively identify the root causes within a very short time, and the train timetables are optimized. The energy and travel time reductions are more than and , respectively. The proposed approaches are worth developing for big data application in large networks.
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