Modified genetic algorithm for simple straight and U-shaped assembly line balancing with fuzzy processing times
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This paper aims at the straight and U-shaped assembly line balancing. Due to the uncertainty, variability and imprecision in actual production systems, the processing time of tasks are presented in triangular fuzzy numbers. In this case, it is intended to optimize the efficiency and idleness percentage of the assembly line as well as and concurrently with minimizing the number of workstations. To solve the problem, a modified genetic algorithm is proposed. One-fifth success rule in selection operator to improve the genetic algorithm performance. This leads genetic algorithm being controlled in convergence and diversity simultaneously by the means of controlling the selective pressure. Also a fuzzy controller in selective pressure employed for one-fifth success rule better implementation in genetic algorithm. In addition, Taguchi design of experiments used for parameter control and calibration. Finally, numerical examples are presented to compare the performance of proposed method with existing ones. Results show the high performance of the proposed algorithm.
KeywordsAssembly line balancing Genetic algorithm One-fifth success rule Fuzzy numbers Taguchi method
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