TY - GEN
T1 - A Two-phase Optimization Model for Autonomous Electric Customized Bus Service Design
AU - Guo, Rongge
AU - Guan, Wei
AU - Bhatnagar, Saumya
AU - Vallati, Mauro
N1 - Funding Information:
1Rongge Guo and Saumya Bhatnagar and Mauro Vallati are with the University of Huddersfield, Huddersfield, United Kingdom. Rongge Guo and Mauro Vallati were supported by a UKRI Future Leaders Fellowship [grant number MR/T041196/1]. [email protected], [email protected], [email protected] 2Wei Guan is with Beijing Jiaotong University, Beijing, China. [email protected]
Publisher Copyright:
© 2022 IEEE.
PY - 2022/11/1
Y1 - 2022/11/1
N2 - Motivated by the requirements of highly effective customized bus (CB) service and by the rapid growth of autonomous electric vehicles (AEVs), this paper studies a new optimization model for the autonomous electric customized bus (AECB) service, aiming at minimizing operating costs and improving vehicles’ efficient use. The proposed model contains two phases: (i) optimization of the vehicle routing, charging operation and passenger-to-vehicle assignment for the fixed travel demands, and (ii) re-optimization of the service according to real-time dynamic travel requests. A solution approach is developed to address the proposed model based on adaptive large neighborhood search (ALNS). The extensive empirical analysis, conducted by considering real-world data on a largescale instance, demonstrates the efficiency of the proposed approach and the quality of the generated solutions.
AB - Motivated by the requirements of highly effective customized bus (CB) service and by the rapid growth of autonomous electric vehicles (AEVs), this paper studies a new optimization model for the autonomous electric customized bus (AECB) service, aiming at minimizing operating costs and improving vehicles’ efficient use. The proposed model contains two phases: (i) optimization of the vehicle routing, charging operation and passenger-to-vehicle assignment for the fixed travel demands, and (ii) re-optimization of the service according to real-time dynamic travel requests. A solution approach is developed to address the proposed model based on adaptive large neighborhood search (ALNS). The extensive empirical analysis, conducted by considering real-world data on a largescale instance, demonstrates the efficiency of the proposed approach and the quality of the generated solutions.
KW - Two-phased optimizationn model
KW - Customized bus (CB)
KW - Autonomous electric vehicles (AEVs)
UR - http://www.scopus.com/inward/record.url?scp=85141828881&partnerID=8YFLogxK
U2 - 10.1109/ITSC55140.2022.9921783
DO - 10.1109/ITSC55140.2022.9921783
M3 - Conference contribution
SN - 9781665468817
SP - 383
EP - 388
BT - 25th IEEE International Conference on Intelligent Transportation Systems
PB - IEEE
ER -
