Uncertain Multi-Objective Hazardous Materials Transport Route Planning Considering Resilience and Low–Carbon
Hazardous material transport accidents are events with a low probability and high consequence risk. With an increase in the proportion of hazardous materials transported on domestic roads, an increasing number of scholars have begun to study this field. In this study, a multi–objective ha...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10016696/ |
| _version_ | 1797861352987951104 |
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| author | Zhanzhong Wang Yan Wang Yuling Jiao |
| author_facet | Zhanzhong Wang Yan Wang Yuling Jiao |
| author_sort | Zhanzhong Wang |
| collection | DOAJ |
| description | Hazardous material transport accidents are events with a low probability and high consequence risk. With an increase in the proportion of hazardous materials transported on domestic roads, an increasing number of scholars have begun to study this field. In this study, a multi–objective hazardous materials transport route planning model considering road traffic resilience and low carbon, which considers the uncertainty of demand and time and is under the limit of the time window. It transports many types of hazardous materials from multiple suppliers to multiple retails with three goals (transportation cost, risk, and carbon emission). This model fills the gap in the research on hazardous material transportation in the field of low carbon, and this is the first time that road traffic resilience is considered in the transport of hazardous materials as one of the weight factors of risk calculation. We designed a improved ant colony optimization algorithm (ACO) to obtain the pareto optimal solution set. We compared the improved ACO with genetic algorithm and simulated annealing algorithm. The results show that the improved ACO has better solution quality and space, which verifies the validity and reliability of the improved ACO. |
| first_indexed | 2024-04-09T22:00:52Z |
| format | Article |
| id | doaj.art-672ac9dcd6bd47f2b4eebd9c015e8c5c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T22:00:52Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-672ac9dcd6bd47f2b4eebd9c015e8c5c2023-03-23T23:00:09ZengIEEEIEEE Access2169-35362023-01-0111269212693110.1109/ACCESS.2023.323679610016696Uncertain Multi-Objective Hazardous Materials Transport Route Planning Considering Resilience and Low–CarbonZhanzhong Wang0Yan Wang1https://orcid.org/0000-0002-8927-5144Yuling Jiao2https://orcid.org/0000-0002-2159-494XTransportation College, Jilin University, Changchun, ChinaTransportation College, Jilin University, Changchun, ChinaTransportation College, Jilin University, Changchun, ChinaHazardous material transport accidents are events with a low probability and high consequence risk. With an increase in the proportion of hazardous materials transported on domestic roads, an increasing number of scholars have begun to study this field. In this study, a multi–objective hazardous materials transport route planning model considering road traffic resilience and low carbon, which considers the uncertainty of demand and time and is under the limit of the time window. It transports many types of hazardous materials from multiple suppliers to multiple retails with three goals (transportation cost, risk, and carbon emission). This model fills the gap in the research on hazardous material transportation in the field of low carbon, and this is the first time that road traffic resilience is considered in the transport of hazardous materials as one of the weight factors of risk calculation. We designed a improved ant colony optimization algorithm (ACO) to obtain the pareto optimal solution set. We compared the improved ACO with genetic algorithm and simulated annealing algorithm. The results show that the improved ACO has better solution quality and space, which verifies the validity and reliability of the improved ACO.https://ieeexplore.ieee.org/document/10016696/Hazardous materials transportationuncertainty theoryant colony optimization algorithmresiliencemulti-objective |
| spellingShingle | Zhanzhong Wang Yan Wang Yuling Jiao Uncertain Multi-Objective Hazardous Materials Transport Route Planning Considering Resilience and Low–Carbon IEEE Access Hazardous materials transportation uncertainty theory ant colony optimization algorithm resilience multi-objective |
| title | Uncertain Multi-Objective Hazardous Materials Transport Route Planning Considering Resilience and Low–Carbon |
| title_full | Uncertain Multi-Objective Hazardous Materials Transport Route Planning Considering Resilience and Low–Carbon |
| title_fullStr | Uncertain Multi-Objective Hazardous Materials Transport Route Planning Considering Resilience and Low–Carbon |
| title_full_unstemmed | Uncertain Multi-Objective Hazardous Materials Transport Route Planning Considering Resilience and Low–Carbon |
| title_short | Uncertain Multi-Objective Hazardous Materials Transport Route Planning Considering Resilience and Low–Carbon |
| title_sort | uncertain multi objective hazardous materials transport route planning considering resilience and low x2013 carbon |
| topic | Hazardous materials transportation uncertainty theory ant colony optimization algorithm resilience multi-objective |
| url | https://ieeexplore.ieee.org/document/10016696/ |
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