@inproceedings{a81910caa265408980c0fb6aff86fd0d,
title = "Bus Scheduling Timetable Optimization Based on Hybrid Bus Sizes",
abstract = "For bus carriers, it is the most basic and important problem to create the bus scheduling timetable based on bus fleet configuration and passenger flow demand. Considering different technical and economic properties, vehicle capacities and limited available number of heterogeneous buses, as well as the time-space characteristics of passenger flow demand, this paper focuses on creating the bus timetables and sizing the buses simultaneously. A bi-objective optimization model is formulated, in which the first objective is to minimum the total operation cost, and the second objective is to maximum the passenger volume. The proposed model is a nonlinear integer programming, thus a genetic algorithm with self-crossover operation is designed to solve it. Finally, a case study in which the model is applied to a real-world case of a bus line in the city of Beijing, China, is presented.",
keywords = "Bus timetable, Fleet configuration, Hybrid sizes, Load factor",
author = "Haitao Yu and Hongguang Ma and Hejia Du and Xiang Li and Randong Xiao and Yong Du",
note = "Publisher Copyright: {\textcopyright} 2018, Springer International Publishing AG.; 17th UK Workshop on Computational Intelligence, UKCI 2017 ; Conference date: 06-09-2017 Through 08-09-2017",
year = "2018",
doi = "10.1007/978-3-319-66939-7\_29",
language = "English",
isbn = "9783319669380",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "337--348",
editor = "Steven Schockaert and Qingfu Zhang and Fei Chao",
booktitle = "Advances in Computational Intelligence Systems - Contributions Presented at the 17th UK Workshop on Computational Intelligence",
address = "Germany",
}