Scheduling flow shops using differential evolution algorithm
By Godfrey C. Onwubolu, D. Davendra. European Journal of Operational Research, 2006
354 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Control and Systems Engineering, Industrial and Manufacturing Engineering
View Paper: https://www.semanticscholar.org/paper/3290451bdd72de9d61f33203d3a759b833d1aa85
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Rubén Ruiz
- Yun-Chia Liang
- Quan-Ke Pan
- Yi Zeng
- Lin Wang
- Mehmet Fatih Tasgetiren
- Tao Chen
- Ponnuthurai Nagaratnam Suganthan
In Control and Systems Engineering
In Industrial and Manufacturing Engineering
- Quan-Ke Pan
- Rubén Ruiz
- Yun-Chia Liang
- Mehmet Fatih Tasgetiren
- Ali Allahverdi
- Eva Vallada
- Bahman Naderi
- Konstantinos E. Parsopoulos
Citations in the corpus
- No-wait flowshop with separate setup times to minimize maximum lateness (2007, background): “[23] or Onwubolu and Davendra [18].”
- Back propagation neural network with adaptive differential evolution algorithm for time series forecasting (2015, background): “DEs and improved DEs are among the best evolutionary algorithms in a variety of fields because of their easy implementation, quick convergence, and robustness (Onwubolu & Davendra, 2006; Qu, Wang, & Zeng, 2013; Wang, He, & Zeng, 2012).”
- A discrete differential evolution algorithm for the permutation flowshop scheduling problem (2007, methodology): “Regarding the applications of differential evolution algorithm to scheduling problems, related literature can be found in [32, 33, 34, 35 ].”