A novel method for green delivery mode considering shared vehicles in the IoT environment

Increasing academic communities and practitioners begin to explore a novel method to reduce environmental pollution and realize green logistics delivery. Additionally, China's Statistical Yearbook shows that the number of private cars has reached 165 million in China. Under this background, this study proposes a green delivery method by the combination of sharing vehicle (private cars) and IoT (Internet of things) from the perspective of vehicle energy efficiency and aims to improve the energy efficiency of social vehicles and provides more convenient delivery services.,This study builds an IoT architecture consisting of customer data layer, information collection layer, cloud optimization layer and delivery task execution layer. Especially in the IoT architecture, a clustering analysis method is used to determine the critical value of customers' classification and shared delivery, a routing optimization method is used to solve the initial solution in could layer and shared technology is used in the implementation of shared delivery.,The results show that the delivery method considering shared vehicles has a positive effect on improving the energy utilization of vehicles. But if all of delivery tasks are performed by the shared vehicle, the application effect may be counterproductive, such as delivery cost increases and energy efficiency decreases. This study provides a good reference for the implementation of green intelligent delivery business, which has a positive effect on the improvement of logistics operation efficiency.,This study designs a novel method to solve the green and shared delivery issues under the IoT environment, which integrates the IoT architecture. The proposed methodology is applied in a real case in China.

• Publication year

  2020

• Venue

  Industrial management & data systems

• Publication date

  2020-07-24

• Fields of study

  Computer Science, Engineering, Environmental Science

• Identifiers
  DOI 10.1108/imds-02-2020-0078
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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