基于遗传退火算法的质检扰动应对方法

For the current situation that the original scheduling scheme lost guidance to workshop production caused by the result of operation quality inspection, a dynamic flexible job-shop scheduling problem with quality inspection operations was studied. Aiming at minimizing the makespan (Cmax) and the differences between the original and updated schemes, a mixed integer programming model was established. To solve the model, a genetic annealing algorithm was proposed. In this algorithm, the concepts of mutation and population of traditional genetic algorithm were introduced into the traditional simulated annealing algorithm. By using the simulated annealing algorithm, the local optimal solution was obtained repeatedly, and the mechanism of large-scale mutation to jump out of the local optimal solution was also acquired, thus the final global near optimal solution was obtained. In addition, considering the scheduling objectives directly in the decoding rules, a decoding mechanism based on situation evaluation was proposed to avoid the generation of poor solutions and reduce the solution space. A software system for algorithmic comparisons was developed to verified the validity of the proposed algorithm.