Details

Autor(en) / Beteiligte

• Wu, Chin-Chia
• Zhang, Xingong
• Azzouz, Ameni
• Shen, Wei-Lun
• Cheng, Shuenn-Ren
• Hsu, Peng-Hsiang
• Lin, Win-Chin

Titel

Metaheuristics for two-stage flow-shop assembly problem with a truncation learning function

Ist Teil von

• Engineering optimization, 2021-05, Vol.53 (5), p.843-866

Ort / Verlag

Abingdon: Taylor & Francis

Erscheinungsjahr

2021

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• This study examines a two-stage three-machine flow-shop assembly scheduling model in which job processing time is considered as a mixed function of a controlled truncation parameter with a sum-of-processing-times-based learning effect. However, the truncation function is very limited in the two-stage flow-shop assembly scheduling settings. To overcome this limitation, this study investigates a two-stage three-machine flow-shop assembly problem with a truncation learning function where the makespan criterion (completion of the last job) is minimized. Given that the proposed model is NP hard, dominance rules, lemmas and a lower bound are derived and applied to the branch-and-bound method. A dynamic differential evolution algorithm, a hybrid greedy iterated algorithm and a genetic algorithm are also proposed for searching approximate solutions. Results obtained from test experiments validate the performance of all the proposed algorithms.