TY - JOUR
T1 - An ‘Internet of Things’ enabled dynamic optimization method for smart vehicles and logistics tasks
AU - Liu, Sichao
AU - Zhang, Yingfeng
AU - Liu, Yang
AU - Wang, Lihui
AU - Wang, Xi Vincent
N1 - Publisher Copyright:
© 2019 Elsevier Ltd
PY - 2019/4/1
Y1 - 2019/4/1
N2 - Centralized and one-way logistics services and the lack of real-time information of logistics resources are common in the logistics industry. This has resulted in the increased logistics cost, energy consumption, logistics resources consumption, and the decreased loading rate. Therefore, it is difficult to achieve efficient, sustainable, and green logistics services with dramatically increasing logistics demands. To deal with such challenges, a real-time information-driven dynamic optimization strategy for smart vehicles and logistics tasks towards green logistics is proposed. Firstly, an ‘Internet of Things’-enabled real-time status sensing model of logistics vehicles is developed. It enables the vehicles to obtain and transmit real-time information to the dynamic distribution center, which manages value-added logistics information. Then, such information can be shared among logistics companies. A dynamic optimization method for smart vehicles and logistics tasks is developed to optimize logistics resources, and achieve a sustainable balance between economic, environmental, and social objectives. Finally, a case study is carried out to demonstrate the effectiveness of the proposed optimization method. The results show that it contributes to reducing logistics cost and fuel consumption, improving vehicles’ utilization rate, and achieving real-time logistics services with high efficiency.
AB - Centralized and one-way logistics services and the lack of real-time information of logistics resources are common in the logistics industry. This has resulted in the increased logistics cost, energy consumption, logistics resources consumption, and the decreased loading rate. Therefore, it is difficult to achieve efficient, sustainable, and green logistics services with dramatically increasing logistics demands. To deal with such challenges, a real-time information-driven dynamic optimization strategy for smart vehicles and logistics tasks towards green logistics is proposed. Firstly, an ‘Internet of Things’-enabled real-time status sensing model of logistics vehicles is developed. It enables the vehicles to obtain and transmit real-time information to the dynamic distribution center, which manages value-added logistics information. Then, such information can be shared among logistics companies. A dynamic optimization method for smart vehicles and logistics tasks is developed to optimize logistics resources, and achieve a sustainable balance between economic, environmental, and social objectives. Finally, a case study is carried out to demonstrate the effectiveness of the proposed optimization method. The results show that it contributes to reducing logistics cost and fuel consumption, improving vehicles’ utilization rate, and achieving real-time logistics services with high efficiency.
KW - Dynamic optimization
KW - Green logistics
KW - Internet of things
KW - Real-time information
UR - http://www.scopus.com/inward/record.url?scp=85060923837&partnerID=8YFLogxK
U2 - 10.1016/j.jclepro.2018.12.254
DO - 10.1016/j.jclepro.2018.12.254
M3 - 文章
AN - SCOPUS:85060923837
SN - 0959-6526
VL - 215
SP - 806
EP - 820
JO - Journal of Cleaner Production
JF - Journal of Cleaner Production
ER -
