An 'Internet of Things' enabled dynamic optimization method for smart vehicles and logistics tasks. (1st April 2019)

• Record Type: Journal Article
• Authors: Liu, Sichao; Zhang, Yingfeng; Liu, Yang; Wang, Lihui; Wang, Xi Vincent
• Is Part Of: Journal of cleaner production. Volume 215(2019)
• Page Start: 806
• Page End: 820
• Publication Date: 2019-04-01
• Subjects: Internet of things -- Green logistics -- Dynamic optimization -- Real-time information; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2018.12.254
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: 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. Highlights: An IoT real-time information sensing model for logistics resources was developed. The model was used for optimal management and allocation of logistics resources. A method was proposed for dynamic optimization of vehicles and tasks. The method contributes to an improved sustainability in the logistics industry.