A Hybrid Bat Algorithm for Solving the Three-Stage Distributed Assembly Permutation Flowshop Scheduling Problem

In this paper, a hybrid bat optimization algorithm based on variable neighbourhood structure and two learning strategies is proposed to solve a three-stage distributed assembly permutation flowshop scheduling problem to minimize the makespan. The algorithm is firstly designed to increase the populat...

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Autores principales:	Jianguo Zheng, Yilin Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	hybrid bat algorithm optimization problem the distributed assembly permutation flowshop scheduling problem variable neighborhood descent Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/ad9d0f036e5846beadae23d328d661cf
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id	oai:doaj.org-article:ad9d0f036e5846beadae23d328d661cf
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spelling	oai:doaj.org-article:ad9d0f036e5846beadae23d328d661cf2021-11-11T15:10:09ZA Hybrid Bat Algorithm for Solving the Three-Stage Distributed Assembly Permutation Flowshop Scheduling Problem10.3390/app1121101022076-3417https://doaj.org/article/ad9d0f036e5846beadae23d328d661cf2021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10102https://doaj.org/toc/2076-3417In this paper, a hybrid bat optimization algorithm based on variable neighbourhood structure and two learning strategies is proposed to solve a three-stage distributed assembly permutation flowshop scheduling problem to minimize the makespan. The algorithm is firstly designed to increase the population diversity by classifying the populations, which solves the difficult trade-off between convergence and diversity of the bat algorithm. Secondly, a selection mechanism is used to update the bat’s velocity and location, solving the difficulty of the algorithm to trade-off exploration and mining capacity. Finally, the Gaussian learning strategy and elite learning strategy assist the whole population to jump out of the local optimal frontier. The simulation results demonstrate that the algorithm proposed in this paper can well solve the DAPFSP. In addition, compared with other metaheuristic algorithms, IHBA has better performance and gives full play to its advantage of finding optimal solutions.Jianguo ZhengYilin WangMDPI AGarticlehybrid bat algorithmoptimization problemthe distributed assembly permutation flowshop scheduling problemvariable neighborhood descentTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10102, p 10102 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	hybrid bat algorithm optimization problem the distributed assembly permutation flowshop scheduling problem variable neighborhood descent Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	hybrid bat algorithm optimization problem the distributed assembly permutation flowshop scheduling problem variable neighborhood descent Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Jianguo Zheng Yilin Wang A Hybrid Bat Algorithm for Solving the Three-Stage Distributed Assembly Permutation Flowshop Scheduling Problem
description	In this paper, a hybrid bat optimization algorithm based on variable neighbourhood structure and two learning strategies is proposed to solve a three-stage distributed assembly permutation flowshop scheduling problem to minimize the makespan. The algorithm is firstly designed to increase the population diversity by classifying the populations, which solves the difficult trade-off between convergence and diversity of the bat algorithm. Secondly, a selection mechanism is used to update the bat’s velocity and location, solving the difficulty of the algorithm to trade-off exploration and mining capacity. Finally, the Gaussian learning strategy and elite learning strategy assist the whole population to jump out of the local optimal frontier. The simulation results demonstrate that the algorithm proposed in this paper can well solve the DAPFSP. In addition, compared with other metaheuristic algorithms, IHBA has better performance and gives full play to its advantage of finding optimal solutions.
format	article
author	Jianguo Zheng Yilin Wang
author_facet	Jianguo Zheng Yilin Wang
author_sort	Jianguo Zheng
title	A Hybrid Bat Algorithm for Solving the Three-Stage Distributed Assembly Permutation Flowshop Scheduling Problem
title_short	A Hybrid Bat Algorithm for Solving the Three-Stage Distributed Assembly Permutation Flowshop Scheduling Problem
title_full	A Hybrid Bat Algorithm for Solving the Three-Stage Distributed Assembly Permutation Flowshop Scheduling Problem
title_fullStr	A Hybrid Bat Algorithm for Solving the Three-Stage Distributed Assembly Permutation Flowshop Scheduling Problem
title_full_unstemmed	A Hybrid Bat Algorithm for Solving the Three-Stage Distributed Assembly Permutation Flowshop Scheduling Problem
title_sort	hybrid bat algorithm for solving the three-stage distributed assembly permutation flowshop scheduling problem
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ad9d0f036e5846beadae23d328d661cf
work_keys_str_mv	AT jianguozheng ahybridbatalgorithmforsolvingthethreestagedistributedassemblypermutationflowshopschedulingproblem AT yilinwang ahybridbatalgorithmforsolvingthethreestagedistributedassemblypermutationflowshopschedulingproblem AT jianguozheng hybridbatalgorithmforsolvingthethreestagedistributedassemblypermutationflowshopschedulingproblem AT yilinwang hybridbatalgorithmforsolvingthethreestagedistributedassemblypermutationflowshopschedulingproblem
_version_	1718437164323176448

A Hybrid Bat Algorithm for Solving the Three-Stage Distributed Assembly Permutation Flowshop Scheduling Problem

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