Name
Title | ||
|---|---|---|
A new history-guided multi-objective evolutionary algorithm based on decomposition for batching scheduling. |
Abstract | ||
|---|---|---|
•A parallel-batch scheduling problem with three objectives is studied.•A history-guided evolutionary algorithm based on decomposition is proposed.•Two novel strategies, local competition and internal replacement, are designed.•Experimental results exhibit the superiority of the proposed algorithm. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.eswa.2019.112920 | Expert Systems with Applications |
Keywords | Field | DocType |
Multi-objective evolutionary algorithm,Constrained scheduling problem,Local competition,Historical information,Elitist preservation | Convergence (routing),Data mining,Population,Mathematical optimization,Tardiness,Job shop scheduling,Evolutionary algorithm,Scheduling (computing),Computer science,Minification,Job scheduler | Journal |
Volume | ISSN | Citations |
141 | 0957-4174 | 1 |
PageRank | References | Authors |
0.35 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhao-Hong Jia | 1 | 57 | 6.70 |
| Le-yang Gao | 2 | 1 | 0.35 |
| Xingyi Zhang | 3 | 101 | 8.59 |