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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.
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]
Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Logistics sustainability practices: an IoT-enabled smart indoor parking system for industrial hazardous chemical vehicles.
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  Data: <searchLink fieldCode="AR" term="%22Zhao%2C+Zhiheng%22">Zhao, Zhiheng</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Mengdi%22">Zhang, Mengdi</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> zhangmengdi99@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Xu%2C+Gangyan%22">Xu, Gangyan</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Dengyin%22">Zhang, Dengyin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+George+Q%2E%22">Huang, George Q.</searchLink><relatesTo>5</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Dec2020, Vol. 58 Issue 24, p7490-7506. 17p. 7 Diagrams, 2 Charts.
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  Data: <searchLink fieldCode="DE" term="%22Hazardous+substances%22">Hazardous substances</searchLink><br /><searchLink fieldCode="DE" term="%22Industrial+districts%22">Industrial districts</searchLink><br /><searchLink fieldCode="DE" term="%22Beacons%22">Beacons</searchLink><br /><searchLink fieldCode="DE" term="%22Tracking+algorithms%22">Tracking algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Tractor+trailer+combinations%22">Tractor trailer combinations</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+of+things%22">Internet of things</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/00207543.2020.1720928
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        Text: English
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      – SubjectFull: Tracking algorithms
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      – SubjectFull: Tractor trailer combinations
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            NameFull: Zhao, Zhiheng
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            NameFull: Xu, Gangyan
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              Text: Dec2020
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              Y: 2020
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