TY - GEN
T1 - Finding robust and consistent space-time delivery paths for multi-day vehicle routing problem
AU - Zhuge, Lijuan
AU - Tong, Lu Carol
AU - Wu, Hailong
AU - Chen, Yiheng
AU - Zhou, Xuesong
N1 - Funding Information:
*Research supported by the National Science Foundation of China (No. 71801006). Lijuan Zhuge is with School of Traffic and Transportation, Beijing Jiaotong University, Haidian, Beijing 100044, China (e-mail: [email protected]).
PY - 2019/10
Y1 - 2019/10
N2 - Facing uncertain and dynamic demands over different days, logistics companies need to adopt adaptive solutions for the last-mile delivery. Meanwhile, one of the major barriers for implementing vehicle routing problem (VRP) algorithms in real world dispatching systems is the stability of delivery driver's routes and schedules. To establish a theoretically sound and practically useful solution framework, this paper aims to optimize robust and consistent space-time paths for the multi-day VRP by providing daily schedules with limited variations from the master schedule. A multi-commodity network flow-based optimization model is proposed to minimize generalized transportation costs and the daily deviation of day-dependent space-time paths among demand analysis zones. Lagrange relaxation (LR) methods and alternating direction method of multipliers (ADMM) are used to handle complex side constraints. Experiments on illustrative networks and Beijing delivery network are developed to demonstrate the effectiveness of the proposed method.
AB - Facing uncertain and dynamic demands over different days, logistics companies need to adopt adaptive solutions for the last-mile delivery. Meanwhile, one of the major barriers for implementing vehicle routing problem (VRP) algorithms in real world dispatching systems is the stability of delivery driver's routes and schedules. To establish a theoretically sound and practically useful solution framework, this paper aims to optimize robust and consistent space-time paths for the multi-day VRP by providing daily schedules with limited variations from the master schedule. A multi-commodity network flow-based optimization model is proposed to minimize generalized transportation costs and the daily deviation of day-dependent space-time paths among demand analysis zones. Lagrange relaxation (LR) methods and alternating direction method of multipliers (ADMM) are used to handle complex side constraints. Experiments on illustrative networks and Beijing delivery network are developed to demonstrate the effectiveness of the proposed method.
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U2 - 10.1109/ITSC.2019.8916849
DO - 10.1109/ITSC.2019.8916849
M3 - Conference contribution
AN - SCOPUS:85076809491
T3 - 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
SP - 1355
EP - 1360
BT - 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
Y2 - 27 October 2019 through 30 October 2019
ER -