ABSTRACT A resilient bus system is crucial for urbanized cities; however, its operation and customer costs are often affected by various disruptions. An optimal bus timetable is needed to improve the bus system's resilience from the cost aspect. This paper proposes a bi‐objective optimization model based on an advanced genetic algorithm (GA), optimizing the company's and passengers' cost. A general disruption model is proposed to describe multiple disruption scenarios, considering various occurrence times and levels. Beijing Bus No. 145 is used to validate the effectiveness of the proposed optimization approach, where the results show that the advanced method reduces the total cost by 11.5% compared with typical methods. After that, the proven method is applied to multi‐disruption scenarios, where the bus system's resilience is measured and optimized. The superiority of the advanced GA is also evident when compared to the particle swarm optimization, whale optimization algorithm and the unmodified GA, particularly under disrupted conditions. This work enriches the theory of bus timetable optimization and bus system resilience measurement and enhancement. The results validate the superiority of the proposed timetable optimization framework and reveal that the bus system resilience is dynamic as the level and occurrence time of disruptions.
Shen et al. (Thu,) studied this question.