Logistics sustainability practices: an IoT-enabled smart indoor parking system for industrial hazardous chemical vehicles.
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| Title: | Logistics sustainability practices: an IoT-enabled smart indoor parking system for industrial hazardous chemical vehicles. |
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| Authors: | Zhao, Zhiheng1,2 (AUTHOR), Zhang, Mengdi1,2 (AUTHOR) zhangmengdi99@gmail.com, Xu, Gangyan3,4 (AUTHOR), Zhang, Dengyin1,2 (AUTHOR), Huang, George Q.5 (AUTHOR) |
| Source: | International Journal of Production Research. Dec2020, Vol. 58 Issue 24, p7490-7506. 17p. 7 Diagrams, 2 Charts. |
| Subjects: | Hazardous substances, Industrial districts, Beacons, Tracking algorithms, Tractor trailer combinations, Internet of things |
| 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| 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. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00207543 |
| DOI: | 10.1080/00207543.2020.1720928 |
