Exploring Maritime Search and Rescue Resource Allocation via an Enhanced Particle Swarm Optimization Method
Maritime search and rescue (SAR) plays a very important role in emergency waterway traffic situations, which is supposed to trigger severe personal casualties and property loss in maritime traffic accidents. The study aims to exploit an optimal allocation strategy with limited SAR resources deployed...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/10/7/906 |
| _version_ | 1827628521740042240 |
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| author | Yang Sun Jun Ling Xinqiang Chen Fancun Kong Qinyou Hu Salvatore Antonio Biancardo |
| author_facet | Yang Sun Jun Ling Xinqiang Chen Fancun Kong Qinyou Hu Salvatore Antonio Biancardo |
| author_sort | Yang Sun |
| collection | DOAJ |
| description | Maritime search and rescue (SAR) plays a very important role in emergency waterway traffic situations, which is supposed to trigger severe personal casualties and property loss in maritime traffic accidents. The study aims to exploit an optimal allocation strategy with limited SAR resources deployed at navigation-constrained coastal islands. The study formulates the problem of SAR resource allocation in coastal areas into a non-linear optimization model. We explore the optimal solution for the SAR resource allocation problem under constraints of different ship and aircraft base station settings with the help of an enhanced particle swarm optimization (EPSO) model. Experimental results suggest that the proposed EPSO model can reasonably allocate the maritime rescue resources with a large coverage area and low time cost. The particle swarm optimization and genetic algorithm are further implemented for the purpose of model performance comparison. The research findings can help maritime traffic regulation departments to make more reasonable decisions for establishing SAR base stations. |
| first_indexed | 2024-03-09T13:34:41Z |
| format | Article |
| id | doaj.art-00498f9ffd6c4d1d994f5adc80363833 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-09T13:34:41Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-00498f9ffd6c4d1d994f5adc803638332023-11-30T21:13:12ZengMDPI AGJournal of Marine Science and Engineering2077-13122022-06-0110790610.3390/jmse10070906Exploring Maritime Search and Rescue Resource Allocation via an Enhanced Particle Swarm Optimization MethodYang Sun0Jun Ling1Xinqiang Chen2Fancun Kong3Qinyou Hu4Salvatore Antonio Biancardo5Merchant Marine College, Shanghai Maritime University, Shanghai 201306, ChinaInstitute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, ChinaInstitute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, ChinaMerchant Marine College, Shanghai Maritime University, Shanghai 201306, ChinaMerchant Marine College, Shanghai Maritime University, Shanghai 201306, ChinaDepartment of Civil, Construction and Environmental Engineering, Federico II University of Naples, 80125 Naples, ItalyMaritime search and rescue (SAR) plays a very important role in emergency waterway traffic situations, which is supposed to trigger severe personal casualties and property loss in maritime traffic accidents. The study aims to exploit an optimal allocation strategy with limited SAR resources deployed at navigation-constrained coastal islands. The study formulates the problem of SAR resource allocation in coastal areas into a non-linear optimization model. We explore the optimal solution for the SAR resource allocation problem under constraints of different ship and aircraft base station settings with the help of an enhanced particle swarm optimization (EPSO) model. Experimental results suggest that the proposed EPSO model can reasonably allocate the maritime rescue resources with a large coverage area and low time cost. The particle swarm optimization and genetic algorithm are further implemented for the purpose of model performance comparison. The research findings can help maritime traffic regulation departments to make more reasonable decisions for establishing SAR base stations.https://www.mdpi.com/2077-1312/10/7/906maritime search and rescuewaterway traffic safetyresource allocation strategyenhanced particle swarm modelconstrained navigation area |
| spellingShingle | Yang Sun Jun Ling Xinqiang Chen Fancun Kong Qinyou Hu Salvatore Antonio Biancardo Exploring Maritime Search and Rescue Resource Allocation via an Enhanced Particle Swarm Optimization Method Journal of Marine Science and Engineering maritime search and rescue waterway traffic safety resource allocation strategy enhanced particle swarm model constrained navigation area |
| title | Exploring Maritime Search and Rescue Resource Allocation via an Enhanced Particle Swarm Optimization Method |
| title_full | Exploring Maritime Search and Rescue Resource Allocation via an Enhanced Particle Swarm Optimization Method |
| title_fullStr | Exploring Maritime Search and Rescue Resource Allocation via an Enhanced Particle Swarm Optimization Method |
| title_full_unstemmed | Exploring Maritime Search and Rescue Resource Allocation via an Enhanced Particle Swarm Optimization Method |
| title_short | Exploring Maritime Search and Rescue Resource Allocation via an Enhanced Particle Swarm Optimization Method |
| title_sort | exploring maritime search and rescue resource allocation via an enhanced particle swarm optimization method |
| topic | maritime search and rescue waterway traffic safety resource allocation strategy enhanced particle swarm model constrained navigation area |
| url | https://www.mdpi.com/2077-1312/10/7/906 |
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