Application of weight coefficient searching genetic algorithm in the plan generation of cargo loading and unloading for liquid cargo carrier

ObjectiveCargo loading and unloading plan generation time and safety evaluation have a great influence on the handling efficiency. To this end, this paper introduce weight coefficient searching genetic algorithm into the plan generation. MethodsLoading safety status mainly involves draft limitation,...

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Main Authors: Ming QI, Jiahao LIN, Miao SUN, Yong ZHANG
Format: Article
Language:English
Published: Editorial Office of Chinese Journal of Ship Research 2022-12-01
Series:Zhongguo Jianchuan Yanjiu
Subjects:
Online Access:http://www.ship-research.com/en/article/doi/10.19693/j.issn.1673-3185.02023
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author Ming QI
Jiahao LIN
Miao SUN
Yong ZHANG
author_facet Ming QI
Jiahao LIN
Miao SUN
Yong ZHANG
author_sort Ming QI
collection DOAJ
description ObjectiveCargo loading and unloading plan generation time and safety evaluation have a great influence on the handling efficiency. To this end, this paper introduce weight coefficient searching genetic algorithm into the plan generation. MethodsLoading safety status mainly involves draft limitation, intact stability checking, hyrostatic strength checking and compatibility between the generated loading and unloading plan and preset process. In this study, a non-deterministic polynomial and corresponding optimization objective functions are established to solve the loading and unloading scheme generation problem of liquid cargo carriers. The weight of each influence factor in the objective function is changed and recorded in sequence to form an optimal objective function cluster. The optimal solution of each member in the cluster is then carried out through an elitist preservation genetic algorithm in order to provide an optimal solution set. On this basis, a comparison is made with the loading and unloading plan of a very large crude oil carrier (VLCC) generated by the linear fitting methodResultsThe ship draft status is more consistent with the target, and the stability state is obviously better than that of the user preset plan in each loading/unloading step generated by this algorithm. ConclusionThe weight coefficient searching genetic algorithm based on elitist preservation proposed in this paper can improve the accuracy and reliability of the liquid cargo carrier loading sequence.
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spelling doaj.art-705f9f705aa04dc89b159e788b55d10e2023-01-18T03:09:49ZengEditorial Office of Chinese Journal of Ship ResearchZhongguo Jianchuan Yanjiu1673-31852022-12-0117Supp1283610.19693/j.issn.1673-3185.02023ZG2023Application of weight coefficient searching genetic algorithm in the plan generation of cargo loading and unloading for liquid cargo carrierMing QI0Jiahao LIN1Miao SUN2Yong ZHANG3Marine Design and Research Institute of China, Shanghai 200011, ChinaMarine Design and Research Institute of China, Shanghai 200011, ChinaMarine Design and Research Institute of China, Shanghai 200011, ChinaMarine Design and Research Institute of China, Shanghai 200011, ChinaObjectiveCargo loading and unloading plan generation time and safety evaluation have a great influence on the handling efficiency. To this end, this paper introduce weight coefficient searching genetic algorithm into the plan generation. MethodsLoading safety status mainly involves draft limitation, intact stability checking, hyrostatic strength checking and compatibility between the generated loading and unloading plan and preset process. In this study, a non-deterministic polynomial and corresponding optimization objective functions are established to solve the loading and unloading scheme generation problem of liquid cargo carriers. The weight of each influence factor in the objective function is changed and recorded in sequence to form an optimal objective function cluster. The optimal solution of each member in the cluster is then carried out through an elitist preservation genetic algorithm in order to provide an optimal solution set. On this basis, a comparison is made with the loading and unloading plan of a very large crude oil carrier (VLCC) generated by the linear fitting methodResultsThe ship draft status is more consistent with the target, and the stability state is obviously better than that of the user preset plan in each loading/unloading step generated by this algorithm. ConclusionThe weight coefficient searching genetic algorithm based on elitist preservation proposed in this paper can improve the accuracy and reliability of the liquid cargo carrier loading sequence.http://www.ship-research.com/en/article/doi/10.19693/j.issn.1673-3185.02023liquid cargo carriercargo loading and unloading planweight coefficient searchingelitist preservationgenetic algorithm
spellingShingle Ming QI
Jiahao LIN
Miao SUN
Yong ZHANG
Application of weight coefficient searching genetic algorithm in the plan generation of cargo loading and unloading for liquid cargo carrier
Zhongguo Jianchuan Yanjiu
liquid cargo carrier
cargo loading and unloading plan
weight coefficient searching
elitist preservation
genetic algorithm
title Application of weight coefficient searching genetic algorithm in the plan generation of cargo loading and unloading for liquid cargo carrier
title_full Application of weight coefficient searching genetic algorithm in the plan generation of cargo loading and unloading for liquid cargo carrier
title_fullStr Application of weight coefficient searching genetic algorithm in the plan generation of cargo loading and unloading for liquid cargo carrier
title_full_unstemmed Application of weight coefficient searching genetic algorithm in the plan generation of cargo loading and unloading for liquid cargo carrier
title_short Application of weight coefficient searching genetic algorithm in the plan generation of cargo loading and unloading for liquid cargo carrier
title_sort application of weight coefficient searching genetic algorithm in the plan generation of cargo loading and unloading for liquid cargo carrier
topic liquid cargo carrier
cargo loading and unloading plan
weight coefficient searching
elitist preservation
genetic algorithm
url http://www.ship-research.com/en/article/doi/10.19693/j.issn.1673-3185.02023
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