Solving a new cost-oriented assembly line balancing problem by classical and hybrid meta-heuristic algorithms

In this study, a new cost-oriented assembly line balancing problem is proposed and formulated. A single objective function consisting of minimizing the cost associated with equipment, labor wage, and station establishment is considered for the problem. This problem is more complicated comparing to t...

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Vydané v:Neural computing & applications Ročník 32; číslo 12; s. 8217 - 8243
Hlavní autori: Salehi, Maryam, Maleki, Hamid Reza, Niroomand, Sadegh
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Springer London 01.06.2020
Springer Nature B.V
Predmet:
Algorithms
Artificial Intelligence
Assembly lines
Balancing
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Computer simulation
Data Mining and Knowledge Discovery
Equipment costs
Genetic algorithms
Heuristic
Heuristic methods
Image Processing and Computer Vision
Optimization
Original Article
Probability and Statistics in Computer Science
Simulated annealing
Tabu search
Taguchi methods
Meta-heuristic algorithm
Assembly line balancing problem
Hungarian method
Taguchi method
ISSN:0941-0643, 1433-3058
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Shrnutí:In this study, a new cost-oriented assembly line balancing problem is proposed and formulated. A single objective function consisting of minimizing the cost associated with equipment, labor wage, and station establishment is considered for the problem. This problem is more complicated comparing to the literature as worker qualification is considered for determining his/her wage. As this problem is of NP-hard optimization problems, some meta-heuristic solution approaches, e.g., simulated annealing, variable neighborhood search, genetic algorithm, tabu search, population-based simulated annealing, and their hybrid versions are proposed. In the proposed algorithms, a novel encoding–decoding scheme is applied. This scheme uses the Hungarian method to assign the workers to the station to reduce the total wage of the workers. To study the performance of the proposed meta-heuristic algorithms, ten test problems are generated randomly, and using one of them the parameters of the algorithms are tuned by the Taguchi method. The final experiments on the proposed algorithms and the test problems show that in the most of the experiments, among the proposed algorithms, the single-solution-based algorithms, except TS, perform better than the population-based algorithms, especially for the case of large size test problems.
Bibliografia:ObjectType-Article-1
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-019-04293-8