An Improved Multiobjective PSO for the Scheduling Problem of Panel Block Construction

An Improved Multiobjective PSO for the Scheduling Problem of Panel Block Construction

Uncertainty is common in ship construction. However, few studies have focused on scheduling problems under uncertainty in shipbuilding. This paper formulates the scheduling problem of panel block construction as a multiobjective fuzzy flow shop scheduling problem (FSSP) with a fuzzy processing time,...

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Main Authors: Zhi Yang, Cungen Liu, Xuefeng Wang, Weixin Qian
Format: Article
Language:English
Published: Hindawi Limited 2016-01-01
Series:Discrete Dynamics in Nature and Society
Online Access:http://dx.doi.org/10.1155/2016/5413520
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spelling doaj-8289864917eb4afaa870ce84cc930aff2020-11-24T23:56:53ZengHindawi LimitedDiscrete Dynamics in Nature and Society1026-02261607-887X2016-01-01201610.1155/2016/54135205413520An Improved Multiobjective PSO for the Scheduling Problem of Panel Block ConstructionZhi Yang0Cungen Liu1Xuefeng Wang2Weixin Qian3State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaState Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaState Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaState Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaUncertainty is common in ship construction. However, few studies have focused on scheduling problems under uncertainty in shipbuilding. This paper formulates the scheduling problem of panel block construction as a multiobjective fuzzy flow shop scheduling problem (FSSP) with a fuzzy processing time, a fuzzy due date, and the just-in-time (JIT) concept. An improved multiobjective particle swarm optimization called MOPSO-M is developed to solve the scheduling problem. MOPSO-M utilizes a ranked-order-value rule to convert the continuous position of particles into the discrete permutations of jobs, and an available mapping is employed to obtain the precedence-based permutation of the jobs. In addition, to improve the performance of MOPSO-M, archive maintenance is combined with global best position selection, and mutation and a velocity constriction mechanism are introduced into the algorithm. The feasibility and effectiveness of MOPSO-M are assessed in comparison with general MOPSO and nondominated sorting genetic algorithm-II (NSGA-II).http://dx.doi.org/10.1155/2016/5413520
collection DOAJ
language English
format Article
sources DOAJ
author Zhi Yang
Cungen Liu
Xuefeng Wang
Weixin Qian
spellingShingle Zhi Yang
Cungen Liu
Xuefeng Wang
Weixin Qian
An Improved Multiobjective PSO for the Scheduling Problem of Panel Block Construction
Discrete Dynamics in Nature and Society
author_facet Zhi Yang
Cungen Liu
Xuefeng Wang
Weixin Qian
author_sort Zhi Yang
title An Improved Multiobjective PSO for the Scheduling Problem of Panel Block Construction
title_short An Improved Multiobjective PSO for the Scheduling Problem of Panel Block Construction
title_full An Improved Multiobjective PSO for the Scheduling Problem of Panel Block Construction
title_fullStr An Improved Multiobjective PSO for the Scheduling Problem of Panel Block Construction
title_full_unstemmed An Improved Multiobjective PSO for the Scheduling Problem of Panel Block Construction
title_sort improved multiobjective pso for the scheduling problem of panel block construction
publisher Hindawi Limited
series Discrete Dynamics in Nature and Society
issn 1026-0226
1607-887X
publishDate 2016-01-01
description Uncertainty is common in ship construction. However, few studies have focused on scheduling problems under uncertainty in shipbuilding. This paper formulates the scheduling problem of panel block construction as a multiobjective fuzzy flow shop scheduling problem (FSSP) with a fuzzy processing time, a fuzzy due date, and the just-in-time (JIT) concept. An improved multiobjective particle swarm optimization called MOPSO-M is developed to solve the scheduling problem. MOPSO-M utilizes a ranked-order-value rule to convert the continuous position of particles into the discrete permutations of jobs, and an available mapping is employed to obtain the precedence-based permutation of the jobs. In addition, to improve the performance of MOPSO-M, archive maintenance is combined with global best position selection, and mutation and a velocity constriction mechanism are introduced into the algorithm. The feasibility and effectiveness of MOPSO-M are assessed in comparison with general MOPSO and nondominated sorting genetic algorithm-II (NSGA-II).
url http://dx.doi.org/10.1155/2016/5413520
work_keys_str_mv AT zhiyang animprovedmultiobjectivepsofortheschedulingproblemofpanelblockconstruction
AT cungenliu animprovedmultiobjectivepsofortheschedulingproblemofpanelblockconstruction
AT xuefengwang animprovedmultiobjectivepsofortheschedulingproblemofpanelblockconstruction
AT weixinqian animprovedmultiobjectivepsofortheschedulingproblemofpanelblockconstruction
AT zhiyang improvedmultiobjectivepsofortheschedulingproblemofpanelblockconstruction
AT cungenliu improvedmultiobjectivepsofortheschedulingproblemofpanelblockconstruction
AT xuefengwang improvedmultiobjectivepsofortheschedulingproblemofpanelblockconstruction
AT weixinqian improvedmultiobjectivepsofortheschedulingproblemofpanelblockconstruction
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