Logistics sustainability practices: an IoT-enabled smart indoor parking system for industrial hazardous chemical vehicles

Abstract

Logistics sustainability practices in industrial cases gain more attention recently especially when transportation efficiency becomes a bottleneck. The research of smart parking develops rapidly especially the thriving of Internet of Things (IoT). In this research, the industrial hazardous chemical vehicle (IHCV) consists of tractor and trailer. The vehicle coupling and decoupling occur frequently in order to fulfil logistics missions. The real-time dynamic indoor location information of both tractors and trailers are of great significance among users. Excessive time and human effort consumed in locating the vehicles lead to the transportation delay and disorderly parking exacerbate congestion inside the indoor parking garage. In this paper, we propose an IoT-enabled smart indoor parking system for logistics vehicles. A self-learning genetic tracking algorithm is developed to ensure the tracking performance. The feasibility and effectiveness of this solution architecture and algorithm are verified in a real-life chemical logistics company. The results show that the proposed algorithm not only performs constant improving location accuracy up to 96.7% after learning but also ensure the long-term use compared to the triangulation method. Moreover, disorderly parking can be identified by location cell partition as to eliminate potential risks. Improved logistics efficiency and lowered congestion situation contribute to the sustainable logistics.

Keywords:

• logistics sustainability
• Internet of Things
• industrial hazardous chemical vehicles
• indoor parking
• self-learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

ORCID

Gangyan Xu http://orcid.org/0000-0001-9537-9006

George Q. Huang http://orcid.org/0000-0003-3592-7917

Additional information

Funding

This research was supported in part by the Natural Science Foundation of Jiangsu Province (grant number BK20180749), Natural Science Research of Jiangsu Higher Education Institutions of China(grant number 19KJB580016), Hong Kong ITF Innovation and Technology Support Program (ITP/079/16LP), National Natural Science Foundation of China (No.61571241 and 61872423) and Nanjing University of Post and Telecommunications research start-up fund (NY218125,NY218126 and NY219112).