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Zhejiang Machinery and Electrical Group

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Flexible job shop scheduling based on improved ant lion algorithm
Published:2022-11-22 author:WANG Yan-jie, XIANG Feng-hong. Browse: 323 Check PDF documents
Flexible job shop scheduling based on improved ant lion algorithm

WANG Yan-jie, XIANG Feng-hong

(School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650000, China)

Abstract: Aiming at the large scale and high complexity of the flexible job shop scheduling problem, based on the traditional ant lion algorithm, an improved ant lion algorithm was proposed to solve the problem. Firstly, a flexible job shop scheduling model with the minimum and maximum completion time as the optimization goal was established, and the double-layer real number coding rules were used to code the processes and machines. Secondly, a hybrid strategy based on chaotic mapping and competition selection was designed. The initial population was randomly generated. Then, the crossover mutation strategy of the genetic algorithm was used to select the process and the machine. Finally, in order to verify the effectiveness of the improved ant lion algorithm in flexible job shop scheduling, a simulation comparison experiment was carried out by using Brandimarte benchmark example and other intelligent algorithms.The research results indicate that the initial population quality of the ant lion algorithm using the hybrid strategy initialization to generate the initial population and the introduction of the crossover mutation strategy is good, the algorithm has a fast convergence speed, a strong ability to escape local optimization, and a high utilization rate of processing machines. In the case of solving Mk01 by the algorithm, the minimum value of the maximum completion time is 40, and the quality of the solution is not inferior to other algorithms.

Key words:  flexible job shop scheduling problem(FJSP); shop scheduling model; ant lion optimization(ALO)algorithm; chaotic mapping; competition selection; crossover mutation strategy; Brandimarte

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