An improved iterated greedy algorithm for the distributed assembly permutation flowshop scheduling problem

•We study a distributed assembly permutation flowshop scheduling problem.•We propose an improved iterative greedy algorithm based on group thinking.•A destruction-construction is designed for products and jobs separately.•A selection method based on the objective values and ages are used.•The effect...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Computers & industrial engineering Ročník 152; s. 107021
Hlavní autori:	Huang, Ying-Ying, Pan, Quan-Ke, Huang, Jiang-Ping, Suganthan, PN, Gao, Liang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.02.2021
Predmet:	Distributed assembly permutation flowshop scheduling Heuristic Iterated greedy algorithm Local search Total flowtime Local search Iterated greedy algorithm Distributed assembly permutation flowshop scheduling Total flowtime Heuristic
ISSN:	0360-8352, 1879-0550
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Popis
Shrnutí:	•We study a distributed assembly permutation flowshop scheduling problem.•We propose an improved iterative greedy algorithm based on group thinking.•A destruction-construction is designed for products and jobs separately.•A selection method based on the objective values and ages are used.•The effectiveness of our algorithm is demonstrated by 810 benchmark instances. This paper considers a distributed assembly permutation flowshop scheduling problem (DAPFSP) with total flowtime (TF) criterion, which is of great significance to both industry and research community. We propose an improved iterative greedy algorithm based on the groupthink (gIGA) for solving the problem. Firstly, based on the solution representation, we present an effective initialization procedure by combining the well-known NEH heuristic and Palmer method. Secondly, in order to improve the efficiency of the algorithm, both the destruction reconstruction process and the local search process are elaborately designed for the products and jobs separately. Next, our algorithm adaptively extracts jobs in the destruction stage with regard to the size of instances. In addition, we employ a novel selection method that is based on the objective values and the ages of the individuals in the population. Through a total of 810 benchmark instances, the proposed algorithm is compared with seven state-of-the-art algorithms in the literature. The experimental results show that the proposed algorithm performs significantly better than the other algorithms in comparison by three analytical methods for solving the DAPFSP with TF criterion.
ISSN:	0360-8352 1879-0550
DOI:	10.1016/j.cie.2020.107021