Storage strategy of outbound containers with uncertain weight by data-driven hybrid genetic simulated annealing algorithm
It is necessary to ensure the ship's stability in container ship stowage and loading and unloading containers. This work aims to reduce the container dumping operation at the midway port and improve the efficiency of ship transportation. Firstly, the constraint problem of the traditional contai...
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| Veröffentlicht in: | PloS one 2023-04, Vol.18 (4), p.e0277890-e0277890 |
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| description | It is necessary to ensure the ship's stability in container ship stowage and loading and unloading containers. This work aims to reduce the container dumping operation at the midway port and improve the efficiency of ship transportation. Firstly, the constraint problem of the traditional container ship stacking is introduced to realize the multi-condition mathematical model of the container ship, container, and wharf. Secondly, a Hybrid Genetic and Simulated Annealing Algorithm (HGSAA) model is proposed for the container stacking and loading stacking in the yard. The specific container space allocation and multi-yard crane adjustment scheme are studied. Finally, the effectiveness of the multi-condition container ship stowage model is verified by numerical experiments by changing the number of outbound containers, storage strategies, storage yards, and bridges. The experimental results show that the HGSAA mode converges to 106.1min at the 751st iteration. Of these, the non-loading and unloading time of yard bridge 1 is 3.43min. The number of operating boxes is 25. The non-loading and unloading time of yard bridge 2 is 3.2min, and the operating box volume is 25 boxes. The objective function of the genetic algorithm converges when it iterates to generation 903 and 107.9min. Among them, the non-loading and unloading time of yard bridge 1 is 4.1min. The non-loading and unloading time of yard bridge 2 is 3.1min. Therefore, the proposed HGSAA has a faster convergence speed than the genetic algorithm and can obtain relatively good results. The proposed container stacking strategy can effectively solve the specific container allocation and multi-yard crane scheduling problems. The finding provides a reference for optimizing container scheduling and improving shipping transportation efficiency. |
| doi_str_mv | 10.1371/journal.pone.0277890 |
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This work aims to reduce the container dumping operation at the midway port and improve the efficiency of ship transportation. Firstly, the constraint problem of the traditional container ship stacking is introduced to realize the multi-condition mathematical model of the container ship, container, and wharf. Secondly, a Hybrid Genetic and Simulated Annealing Algorithm (HGSAA) model is proposed for the container stacking and loading stacking in the yard. The specific container space allocation and multi-yard crane adjustment scheme are studied. Finally, the effectiveness of the multi-condition container ship stowage model is verified by numerical experiments by changing the number of outbound containers, storage strategies, storage yards, and bridges. The experimental results show that the HGSAA mode converges to 106.1min at the 751st iteration. Of these, the non-loading and unloading time of yard bridge 1 is 3.43min. The number of operating boxes is 25. The non-loading and unloading time of yard bridge 2 is 3.2min, and the operating box volume is 25 boxes. The objective function of the genetic algorithm converges when it iterates to generation 903 and 107.9min. Among them, the non-loading and unloading time of yard bridge 1 is 4.1min. The non-loading and unloading time of yard bridge 2 is 3.1min. Therefore, the proposed HGSAA has a faster convergence speed than the genetic algorithm and can obtain relatively good results. The proposed container stacking strategy can effectively solve the specific container allocation and multi-yard crane scheduling problems. The finding provides a reference for optimizing container scheduling and improving shipping transportation efficiency.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0277890</identifier><identifier>PMID: 37027422</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Analysis ; Biology and Life Sciences ; Boxes ; Cargo ships ; Container ships ; Containers ; Convergence ; Cranes ; Dumping ; Energy consumption ; Engineering and Technology ; Genetic algorithms ; Genetic research ; Iterative methods ; Linear programming ; Management ; Mathematical models ; Models, Theoretical ; Mutation ; Numerical experiments ; Objective function ; Optimization ; Packing problem ; Physical Sciences ; Ports ; Research and Analysis Methods ; Scheduling ; Ships ; Simulated annealing ; Space allocation ; Stacking ; Storage ; Stowage (onboard equipment) ; Transportation ; Unloading</subject><ispartof>PloS one, 2023-04, Vol.18 (4), p.e0277890-e0277890</ispartof><rights>Copyright: © 2023 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2023 Public Library of Science</rights><rights>2023 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2023 Wang et al 2023 Wang et al</rights><rights>2023 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c642t-1ab3d97650183a9e56238cc8e3cdc6a9a17716b97f156ce93d900cb4b7ea01d83</cites><orcidid>0000-0002-6431-9374</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10081779/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10081779/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79342,79343</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37027422$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wang, Ruoqi</creatorcontrib><creatorcontrib>Li, Jiawei</creatorcontrib><creatorcontrib>Bai, Ruibin</creatorcontrib><creatorcontrib>Wang, Lei</creatorcontrib><title>Storage strategy of outbound containers with uncertain weight by data-driven hybrid genetic simulated annealing algorithm</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>It is necessary to ensure the ship's stability in container ship stowage and loading and unloading containers. This work aims to reduce the container dumping operation at the midway port and improve the efficiency of ship transportation. Firstly, the constraint problem of the traditional container ship stacking is introduced to realize the multi-condition mathematical model of the container ship, container, and wharf. Secondly, a Hybrid Genetic and Simulated Annealing Algorithm (HGSAA) model is proposed for the container stacking and loading stacking in the yard. The specific container space allocation and multi-yard crane adjustment scheme are studied. Finally, the effectiveness of the multi-condition container ship stowage model is verified by numerical experiments by changing the number of outbound containers, storage strategies, storage yards, and bridges. The experimental results show that the HGSAA mode converges to 106.1min at the 751st iteration. Of these, the non-loading and unloading time of yard bridge 1 is 3.43min. The number of operating boxes is 25. The non-loading and unloading time of yard bridge 2 is 3.2min, and the operating box volume is 25 boxes. The objective function of the genetic algorithm converges when it iterates to generation 903 and 107.9min. Among them, the non-loading and unloading time of yard bridge 1 is 4.1min. The non-loading and unloading time of yard bridge 2 is 3.1min. Therefore, the proposed HGSAA has a faster convergence speed than the genetic algorithm and can obtain relatively good results. The proposed container stacking strategy can effectively solve the specific container allocation and multi-yard crane scheduling problems. The finding provides a reference for optimizing container scheduling and improving shipping transportation efficiency.</description><subject>Algorithms</subject><subject>Analysis</subject><subject>Biology and Life Sciences</subject><subject>Boxes</subject><subject>Cargo ships</subject><subject>Container ships</subject><subject>Containers</subject><subject>Convergence</subject><subject>Cranes</subject><subject>Dumping</subject><subject>Energy consumption</subject><subject>Engineering and Technology</subject><subject>Genetic algorithms</subject><subject>Genetic research</subject><subject>Iterative methods</subject><subject>Linear programming</subject><subject>Management</subject><subject>Mathematical models</subject><subject>Models, Theoretical</subject><subject>Mutation</subject><subject>Numerical experiments</subject><subject>Objective function</subject><subject>Optimization</subject><subject>Packing problem</subject><subject>Physical 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strategy of outbound containers with uncertain weight by data-driven hybrid genetic simulated annealing algorithm</title><author>Wang, Ruoqi ; Li, Jiawei ; Bai, Ruibin ; Wang, Lei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c642t-1ab3d97650183a9e56238cc8e3cdc6a9a17716b97f156ce93d900cb4b7ea01d83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Analysis</topic><topic>Biology and Life Sciences</topic><topic>Boxes</topic><topic>Cargo ships</topic><topic>Container ships</topic><topic>Containers</topic><topic>Convergence</topic><topic>Cranes</topic><topic>Dumping</topic><topic>Energy consumption</topic><topic>Engineering and Technology</topic><topic>Genetic algorithms</topic><topic>Genetic research</topic><topic>Iterative methods</topic><topic>Linear programming</topic><topic>Management</topic><topic>Mathematical models</topic><topic>Models, 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algorithm</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2023-04-07</date><risdate>2023</risdate><volume>18</volume><issue>4</issue><spage>e0277890</spage><epage>e0277890</epage><pages>e0277890-e0277890</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>It is necessary to ensure the ship's stability in container ship stowage and loading and unloading containers. This work aims to reduce the container dumping operation at the midway port and improve the efficiency of ship transportation. Firstly, the constraint problem of the traditional container ship stacking is introduced to realize the multi-condition mathematical model of the container ship, container, and wharf. Secondly, a Hybrid Genetic and Simulated Annealing Algorithm (HGSAA) model is proposed for the container stacking and loading stacking in the yard. The specific container space allocation and multi-yard crane adjustment scheme are studied. Finally, the effectiveness of the multi-condition container ship stowage model is verified by numerical experiments by changing the number of outbound containers, storage strategies, storage yards, and bridges. The experimental results show that the HGSAA mode converges to 106.1min at the 751st iteration. Of these, the non-loading and unloading time of yard bridge 1 is 3.43min. The number of operating boxes is 25. The non-loading and unloading time of yard bridge 2 is 3.2min, and the operating box volume is 25 boxes. The objective function of the genetic algorithm converges when it iterates to generation 903 and 107.9min. Among them, the non-loading and unloading time of yard bridge 1 is 4.1min. The non-loading and unloading time of yard bridge 2 is 3.1min. Therefore, the proposed HGSAA has a faster convergence speed than the genetic algorithm and can obtain relatively good results. The proposed container stacking strategy can effectively solve the specific container allocation and multi-yard crane scheduling problems. The finding provides a reference for optimizing container scheduling and improving shipping transportation efficiency.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>37027422</pmid><doi>10.1371/journal.pone.0277890</doi><tpages>e0277890</tpages><orcidid>https://orcid.org/0000-0002-6431-9374</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Analysis Biology and Life Sciences Boxes Cargo ships Container ships Containers Convergence Cranes Dumping Energy consumption Engineering and Technology Genetic algorithms Genetic research Iterative methods Linear programming Management Mathematical models Models, Theoretical Mutation Numerical experiments Objective function Optimization Packing problem Physical Sciences Ports Research and Analysis Methods Scheduling Ships Simulated annealing Space allocation Stacking Storage Stowage (onboard equipment) Transportation Unloading |
| title | Storage strategy of outbound containers with uncertain weight by data-driven hybrid genetic simulated annealing algorithm |
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