An 'Internet of Things' enabled dynamic optimization method for smart vehicles and logistics tasks

JOURNAL OF CLEANER PRODUCTION

卷:215

Article

Liu, Sichao¹  Zhang, Yingfeng^2,3  Liu, Yang^4,5  Wang, Lihui¹  Wang, Xi Vincent¹ 

[1] KTH Royal Inst Technol, Dept Prod Engn, Stockholm, Sweden

[2] Northwestern Polytech Univ, Key Lab Contemporary Design & Integrated Mfg Tech, Minist Educ, Xian 710072, Shaanxi, Peoples R China

[3] Southern Univ Sci & Technol, Dept Mech & Energy Engn, Shenzhen 518055, Guangdong, Peoples R China

[4] Linkoping Univ, Dept Management & Engn, SE-58183 Linkoping, Sweden

[5] Univ Vaasa, Dept Prod, Vaasa 65200, Finland

关键词: Internet of things;    Green logistics;    Dynamic optimization;    Real-time information;   

DOI  :  10.1016/j.jclepro.2018.12.254

来源: Elsevier

【 摘 要 】

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. (C) 2019 Elsevier Ltd. All rights reserved.

【 授权许可】

Free