A Novel Production Scheduling Approach Based on Improved Hybrid Genetic Algorithm

Lili Dai, He Lu, Dezheng Hua, Xinhua Liu (), Hongming Chen, Adam Glowacz, Grzegorz Królczyk and Zhixiong Li ()
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Lili Dai: Institute of Smart Materials and Applied Technology, Lianyungang Normal College, Lianyungang 222006, China
He Lu: Institute of Smart Materials and Applied Technology, Lianyungang Normal College, Lianyungang 222006, China
Dezheng Hua: School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 211006, China
Xinhua Liu: School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 211006, China
Hongming Chen: School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 211006, China
Adam Glowacz: Department of Automatic, Control and Robotics, AGH University of Science and Technology, 30-059 Kraków, Poland
Grzegorz Królczyk: Faculty of Mechanical Engineering, Opole University of Technology, 45-758 Opole, Poland
Zhixiong Li: Faculty of Mechanical Engineering, Opole University of Technology, 45-758 Opole, Poland

Sustainability, 2022, vol. 14, issue 18, 1-15

Abstract: Due to the complexity of the production shop in discrete manufacturing industry, the traditional genetic algorithm (GA) cannot solve the production scheduling problem well. In order to enhance the GA-based method to solve the production scheduling problem effectively, the simulated annealing algorithm (SAA) is used to develop an improved hybrid genetic algorithm. Firstly, the crossover probability and mutation probability of the genetic operation are adjusted, and the elite replacement operation is adopted for simulated annealing operator. Then, a mutation method is used for the comparison and replacement of the genetic operations to obtain the optimal value of the current state. Lastly, the proposed hybrid genetic algorithm is compared with several scheduling algorithms, and the superiority and efficiency of the proposed method are verified in solving the production scheduling.

Keywords: production scheduling; hybrid genetic algorithm; artificial intelligence; sustainable design; discrete manufacturing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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