TY - GEN
T1 - Multi-objective Vehicle Routing Problem with Simultaneous Pick-up and Delivery Considering Customer Satisfaction
AU - Yan, Xiaoyuan
AU - Xiao, Boping
AU - Zhao, Zhonghao
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/4
Y1 - 2019/4
N2 - The vehicle routing problem involves the distribution of orders to customers over a time window by collected vehicles. As demand tends to be individualized and diversified, customer satisfaction weighs most in a company's standing interests, better sacrifice costs in some cases to satisfy customers, while the simultaneous delivery and pick-up emerges as resource-efficient and time-saving. In this case, an request composed of a given delivery, followed by a needed pick-up at one customer location. The optimization problem is modeled as a multi-objective model with simultaneous delivery and pick-up based on an improved fuzzy due-time window. The measurement of objective is in the form of minimizing the total length of vehicles' paths and the total service costs, as well as maximizing the sum of all customer satisfactions to enhance competitive service quality. Finally, an improved genetic algorithm is implemented to resolve the multi-objective vehicle scheduling problem. Computational results of a numerical example are performed to indicate the validity of the proposed model, showing that our suggested algorithm can produce improved customer- satisfied routes without substantially adding the total service costs and total distance travelled.
AB - The vehicle routing problem involves the distribution of orders to customers over a time window by collected vehicles. As demand tends to be individualized and diversified, customer satisfaction weighs most in a company's standing interests, better sacrifice costs in some cases to satisfy customers, while the simultaneous delivery and pick-up emerges as resource-efficient and time-saving. In this case, an request composed of a given delivery, followed by a needed pick-up at one customer location. The optimization problem is modeled as a multi-objective model with simultaneous delivery and pick-up based on an improved fuzzy due-time window. The measurement of objective is in the form of minimizing the total length of vehicles' paths and the total service costs, as well as maximizing the sum of all customer satisfactions to enhance competitive service quality. Finally, an improved genetic algorithm is implemented to resolve the multi-objective vehicle scheduling problem. Computational results of a numerical example are performed to indicate the validity of the proposed model, showing that our suggested algorithm can produce improved customer- satisfied routes without substantially adding the total service costs and total distance travelled.
KW - customer satisfaction
KW - delivery and Pickup
KW - genetic algorithm
KW - improved time windows
KW - multi-objective
KW - vehicle routing problem
UR - https://www.scopus.com/pages/publications/85079242530
U2 - 10.1109/SMILE45626.2019.8965319
DO - 10.1109/SMILE45626.2019.8965319
M3 - 会议稿件
AN - SCOPUS:85079242530
T3 - Proceedings - 2019 IEEE International Conference on Smart Manufacturing, Industrial and Logistics Engineering, SMILE 2019
SP - 93
EP - 97
BT - Proceedings - 2019 IEEE International Conference on Smart Manufacturing, Industrial and Logistics Engineering, SMILE 2019
A2 - Chien, Chen-Fu
A2 - Tang, Renzhong
A2 - Dou, Runliang
A2 - Wu, Jei-Zheng
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Smart Manufacturing, Industrial and Logistics Engineering, SMILE 2019
Y2 - 20 April 2019 through 21 April 2019
ER -