分类： 工程与技术科学 >> 工程数学 提交时间： 2024-05-10
Shoujing Zhang
Tiantian Hou
Qing Qu
Adam Glowacz
Samar M. Alqhtani
Muhammad Irfan
Grzegorz Królczyk
Z Li
摘要： Aiming at the distributed flexible job shop scheduling problem under dual resource constraints considering the influence of workpiece transportation time between factories and machines, a distributed flexible job shop scheduling problem (DFJSP) model with the optimization goal of minimizing completion time is established, and an improved mayfly algorithm (IMA) is proposed to solve it. Firstly, the mayfly position vector is discrete mapped to make it applicable to the scheduling problem. Secondly, three-layer coding rules of process, worker and machine is adopted, in which the factory selection is reflected by machine number, according to the characteristics of the model, a hybrid initialization strategy is designed to improve the population quality and diversity; Thirdly, an active time window decoding strategy considering transportation time is designed for the worker-machine idle time window to improve the local optimization performance of the algorithm; In addition, The improved crossover and mutation operators is designed to expand the global search range of the algorithm. Finally, through simulation experiments, the results of various algorithms are compared to verify the effectiveness of the proposed algorithm for isomorphism and isomerism factories instances.
分类： 工程与技术科学 >> 工程通用技术 提交时间： 2024-05-10
摘要： Due to the complexity of the production shop in discrete manufacturing industry, 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, 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.
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