Departure timetable optimization based on NSGA-II

This paper utilizes the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with an elite strategy for multi-objective optimization of bus timetables. In this study, three optimization objectives - passenger travel time, bus operation cost, and bus resource efficiency - are common and conflicting goals in optimizing bus route services. The algorithm's findings indicate that the optimized timetable leads to a 10.2% reduction in passenger travel time, a 4% decrease in public transportation operating costs, and an 8.4% reduction in the index of public transport resource efficiency, thereby achieving partial optimization of the objectives. This notable improvement demonstrates the potential of multi-objective optimization methods in bus operation management.