Title
Multi-objective flow shop scheduling with limited buffers using hybrid self-adaptive differential evolution
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.
Year
DOI
Venue
2019
10.1007/s12293-019-00290-5
Memetic Computing
Keywords
Field
DocType
Parameter self-adaptive, Differential evolution, Local search operators, Flow shop scheduling, Multi-objective optimization
Convergence (routing),Mathematical optimization,Job shop scheduling,Scheduling (computing),Flow shop scheduling,Multi-objective optimization,Differential evolution,Self adaptive,Local search (optimization),Mathematics
Journal
Volume
Issue
ISSN
11
4
1865-9292
Citations 
PageRank 
References 
1
0.35
0
Authors
7
Name
Order
Citations
PageRank
Jing J. Liang12073107.92
Peng Wang210.35
Li Guo310.35
Bo-Yang Qu4121546.32
Caitong Yue510.35
kunjie yu6705.36
Yachao Wang710.35