Abstract
This paper focuses on how to minimize the total passenger travel time cost by computing and adjusting skip-sop patterns with given time-varying origin-to-destination passenger demand matrices. A bi-objective nonlinear integer programming model with linear constraints is proposed to precisely formulate the total travel time cost and operating cost under minute-dependent demand from the different origin–destination pairs. The proposed model is implemented by using the genetic algorithm with the idea point optimization solvers, and we show its effectiveness on the real world instance of Guangzhou Metro Line 8.
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