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Memetic Computing
Erschienen in: Memetic Computing 4/2019

16.07.2019 | Regular Research Paper

Multi-objective flow shop scheduling with limited buffers using hybrid self-adaptive differential evolution

verfasst von: Jing Liang, Peng Wang, Li Guo, Boyang Qu, Caitong Yue, Kunjie Yu, Yachao Wang

Erschienen in: Memetic Computing | Ausgabe 4/2019

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Abstract

In this paper, a self-adaptive differential evolution (DE) algorithm is designed to solve multi-objective flow shop scheduling problems with limited buffers (FSSPwLB). The makespan and the largest job delay are treated as two separate objectives which are optimized simultaneously. To improve the performance of the proposed algorithm and eliminate the difficulty of setting parameters, an adaptive mechanism is designed and incorporated into DE. Moreover, various local search and hybrid meta-heuristic methods are presented and compared to improve the convergence. Through the analysis of the experimental results, the proposed algorithm is able to tackle the FSSPwLB problems effectively by generating superior and stable scheduling strategies.
Vorheriger Artikel Project portfolio selection and scheduling under a fuzzy environment
Nächster Artikel Estimation of distribution evolution memetic algorithm for the unrelated parallel-machine green scheduling problem

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Metadaten
Titel
Multi-objective flow shop scheduling with limited buffers using hybrid self-adaptive differential evolution
verfasst von
Jing Liang
Peng Wang
Li Guo
Boyang Qu
Caitong Yue
Kunjie Yu
Yachao Wang
Publikationsdatum
16.07.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
Memetic Computing / Ausgabe 4/2019
Print ISSN: 1865-9284
Elektronische ISSN: 1865-9292
DOI
https://doi.org/10.1007/s12293-019-00290-5

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