- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
Currently, typical challenges that logistics industry faces include the exploding logistics (including reverse logistics) tasks, the lack of real-time and accurate logistics information, and demands towards sustainable logistics. Therefore, it is difficult for logistic companies to achieve highlyefficient and sustainable reverse logistics. This paper adopts a bottom-up logistics strategy that aims to achieve the real-time information-driven dynamic optimisation distribution for logistics tasks. Under this strategic framework, an IoT-enabled real-time information sensing model is designed to sense and capture the real-time data of logistics resources, which are shared among companies after the value-added processes. Realtime information-driven dynamic optimisation for logistics tasks is proposed to optimise the configuration of logistics resources, reduce logistics cost, energy consumption and the distribution distance, and alleviate the environmental pollution. The objective of this research is to develop an innovative logistics distribution model for sustainable logistics.
This paper proposes a systematic architecture of real-time information-driven dynamic optimisation for sustainable reverse logistics. The Internet of Things technology is used to build IoT-enabled environment for sensing and capturing the real-time, accurate, and consistent information of logistics resources. The captured logistics information could be processed to achieve the added value and managed by the logistics enterprise resources management system. The valueadded logistics information could be shared among the enterprises. A real-time information-driven dynamic optimisation for logistics tasks is proposed to optimise the logistics tasks and vehicles and achieve the optimal allocation between tasks and vehicles under constraints. RFID-based loading verification service and real-time information-enabled routing optimisation and navigation services are constructed for logistics vehicles, and to avoid the incorrect loading of logistics tasks. The proposed method can reduce the total logistics cost, energy consumption, and the total logistics distance, optimise the configuration of logistics resources, and achieve sustainable reverse logistics services.