Metaheuristics for two-stage flow-shop assembly problem with a truncation learning function. Issue 5 (4th May 2021)

• Record Type: Journal Article
• Authors: Wu, Chin-Chia; Zhang, Xingong; Azzouz, Ameni; Shen, Wei-Lun; Cheng, Shuenn-Ren; Hsu, Peng-Hsiang; Lin, Win-Chin
• Is Part Of: Engineering optimization. Volume 53:Issue 5(2021)
• Page Start: 843
• Page End: 866
• Publication Date: 2021-05-04
• Subjects: Two-stage assembly -- dynamic differential evolution algorithm -- genetic algorithm -- iterated greedy algorithm; Engineering design -- Periodicals; Mathematical optimization -- Periodicals; 620.0042
• DOI: 10.1080/0305215X.2020.1757089
• Languages: English
• ISSNs: 0305-215X

Abstract

ABSTRACT: 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.