A branch-and-bound algorithm and four metaheuristics for minimizing total completion time for a two-stage assembly flow-shop scheduling problem with learning consideration

This article addresses a two-stage, three-machine assembly scheduling problem that considers the learning effect. All jobs are processed on two machines in the first stage and move on to be processed on an assembly machine in the second stage. The objective of the study is to minimize the total comp...

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Vydané v:Engineering optimization Ročník 52; číslo 6; s. 1009 - 1036
Hlavní autori: Wu, Chin-Chia, Bai, Danyu, Azzouz, Ameni, Chung, I.-Hong, Cheng, Shuenn-Ren, Jhwueng, Dwueng-Chwuan, Lin, Win-Chin, Said, Lamjed Ben
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Abingdon Taylor & Francis 02.06.2020
Taylor & Francis Ltd
Predmet:
Algorithms
Assembly machines
Completion time
Computer simulation
genetic algorithm
Genetic algorithms
Greedy algorithms
Heuristic methods
Job shop scheduling
Job shops
Learning
Lower bounds
Simulated annealing
Two-stage assembly
ISSN:0305-215X, 1029-0273
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Shrnutí:This article addresses a two-stage, three-machine assembly scheduling problem that considers the learning effect. All jobs are processed on two machines in the first stage and move on to be processed on an assembly machine in the second stage. The objective of the study is to minimize the total completion time of the given jobs. Because the problem is NP hard, the authors first established a lower bound and several adjacent propositions using a branch-and-bound algorithm to search for the optimal solution. Four metaheuristics are proposed to approximate the solutions: genetic algorithms, cloud theory-based simulated annealing, artificial bee colonies and iterated greedy algorithms. Four different heuristics are used as seeds in each metaheuristic to obtain high-quality approximate solutions. The performances of all 16 metaheuristics and the branch-and-bound algorithm are then examined and are reported herein.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0305-215X
1029-0273
DOI:10.1080/0305215X.2019.1632303