TY - GEN
T1 - Fuzzy Bi-objective Chance-Constrained Programming Model for Timetable Optimization of a Bus Route
AU - Du, Hejia
AU - Ma, Hongguang
AU - Li, Xiang
N1 - Publisher Copyright:
© 2018, Springer International Publishing AG.
PY - 2018
Y1 - 2018
N2 - Timetable optimization is essential to the improvement of a bus operating company’s economic profits, quality of service and competitiveness in the market. The most previous researches studied the bus timetabling with assuming the passenger demand is certain but it varies in practice. In this study, we consider a timetable optimization problem of a single bus line under fuzzy environment. Assuming the passenger quantity in per time segment is a fuzzy value, a fuzzy bi-objective programming model that maximizes the total passenger volume and minimizes the total bus travel time under a capacity rate constraint is established. This chance constrained programming model is formulated with the passenger volume and capacity rate under certain chance constraints. Furthermore, a genetic algorithm of variable length is designed to solve the proposed model. Finally, we present a case study that utilizing real data obtained from a major Beijing bus operating company to illustrate the proposed model and algorithm.
AB - Timetable optimization is essential to the improvement of a bus operating company’s economic profits, quality of service and competitiveness in the market. The most previous researches studied the bus timetabling with assuming the passenger demand is certain but it varies in practice. In this study, we consider a timetable optimization problem of a single bus line under fuzzy environment. Assuming the passenger quantity in per time segment is a fuzzy value, a fuzzy bi-objective programming model that maximizes the total passenger volume and minimizes the total bus travel time under a capacity rate constraint is established. This chance constrained programming model is formulated with the passenger volume and capacity rate under certain chance constraints. Furthermore, a genetic algorithm of variable length is designed to solve the proposed model. Finally, we present a case study that utilizing real data obtained from a major Beijing bus operating company to illustrate the proposed model and algorithm.
KW - Bi-objective programming
KW - Fuzzy chance-constrained programming
KW - Genetic algorithm
KW - Timetable optimization
UR - https://www.scopus.com/pages/publications/85029592414
U2 - 10.1007/978-3-319-66939-7_27
DO - 10.1007/978-3-319-66939-7_27
M3 - Conference contribution
AN - SCOPUS:85029592414
SN - 9783319669380
T3 - Advances in Intelligent Systems and Computing
SP - 312
EP - 324
BT - Advances in Computational Intelligence Systems - Contributions Presented at the 17th UK Workshop on Computational Intelligence
A2 - Schockaert, Steven
A2 - Zhang, Qingfu
A2 - Chao, Fei
PB - Springer Verlag
T2 - 17th UK Workshop on Computational Intelligence, UKCI 2017
Y2 - 6 September 2017 through 8 September 2017
ER -