2021

Journal

Engineering optimization, 53(5), 843-866

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.

@article{wu2021metaheuristics,
  title={Metaheuristics for two-stage flow-shop assembly problem with a truncation learning function},
  author={Wu, Chin-Chia and Zhang, Xingong and Azzouz, Ameni and Shen, Wei-Lun and Cheng, Shuenn-Ren and Hsu, Peng-Hsiang and Lin, Win-Chin},
  journal={Engineering optimization},
  volume={53},
  number={5},
  pages={843--866},
  year={2021},
  publisher={Taylor \& Francis}
}