关键词: 杂草算法, 变邻域搜索, 行为运作, 多目标优化, 柔性作业车间

Abstract: To study multi-objective flexible job-shop problem with heterogeneous Dejong learning effect,variable neighborhood invasive weed optimization was proposed.Based on random key method,one two-section weed coding method was used to solve scheduling,routing and assignment problems.The fitness of invasive weed was calculated according to grey entropy correlation grade.New parent population was produced based on preference and crowded distance.Three kinds of neighborhood structure were constructed to search elite individuals with variable neighborhood search strategy at the end of iteration.The algorithm was used to solve Kacem benchmark problems and flexible job-shop problem with Dejong learning effect.Results showed that the proposed algorithm could solve above problems,and the total completion time was more sensitive to M value.Decision makers should pay attention to the heterogeneous learning effect in the flexible job-shop problem.Suitable workers should be assigned to the right station to improve the operation level and reduce the labor cost.

Key words: invasive weed optimization, variable neighborhood search, behavior management, multi-objective optimizing, flexible job-shop