A Genetic Algorithm for Hybrid Flow Shop Scheduling

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Sunjita Thongkerd
Choosak Pornsing


The objective of this research is to apply a genetic algorithm for a hybrid flow shop scheduling problem in a steel plate production company. The manufacturing orders from customers are different in the characters of sizes and quantities. Besides, there are 3 main processes; slitter process, shear process, and squared shear process. Moreover, processes have unequal number of machines and production rates. The conditions are the products must go thru all processes sequentially and can select any machines in the process. The author applied a genetic algorithm with two objective functions; minimized makespan ( ) and minimized mean flowtime ( ). The algorithm was tested with the problem size of 5, 10, 15, and 20 orders in order to be compared with the current method of the company. The results showed that the genetic algorithm could decreased the average makespan by 12.99% and decreased the average mean flowtime by 26.48%. Moreover, it found that the production planning time was decreased by 58.74% when compared with the current method. In conclusion, the genetic algorithm could reduce makespan, mean flowtime, and production planning time significantly.

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How to Cite
Thongkerd, S. ., & Pornsing, C. . (2020). A Genetic Algorithm for Hybrid Flow Shop Scheduling. Naresuan University Engineering Journal, 15(1), 33–43. Retrieved from https://ph01.tci-thaijo.org/index.php/nuej/article/view/236366
Research Paper


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