【摘要】 针对各工件目标不同的多目标柔性Job Shop调度问题,提出了一种基于混合遗传算法的优化求解方法。首先建立了该类问题的调度模型;然后,在基本遗传算法柜架的基础上,通过两层意义上的随机权重法,将多目标问题随机转化为单目标问题,同时为了保证算法的收敛性和Pareto解的多样性,混合遗传算法集成了精英保留策略和小生境技术;利用层次分析法与模糊综合评判集成的方法,从Pareto解集中选出最优妥协解。最后通过实验仿真,证明提出的方法可以有效解决该类多目标柔性Job Shop调度问题。更多还原

【Abstract】 Purpose.To our best knowledge,there are only five papers in the open literature that attempted to solve job-oriented multi-objective FJSP（Flexible Job Shop Scheduling Problem）.We now propose an integrated genetic algorithm that,in our opinion,can solve such problem better.In the full paper,we explain in detail our algorithm;in this abstract,we just list the two topics of our explanation:（1） scheduling model;（2） job-oriented multi-objective algorithm for optimizing the solution of job-oriented multi-objective FJSP;its two subtopics are the line of thinking of our algorithm（2.1） and detailed design of our algorithm（2.2）;the subsubtopics of subtopic（2.2） are encoding（2.2.1）,initial set（2.2.2）,the eight steps of the decoding operation（2.2.3）,the calculation of fitness（2.2.4）,the selection operation（2.2.5）,the crossover operation（2.2.6）,the mutation operation（2.2.7） and the terminal condition（2.2.8）.Finally,we give a numerical simulation example,whose results are given in Tables 1 through 3 in the full paper and shown in Fig.2 of the full paper giving the Gantt chart of the best-compromise solution.These results show preliminarily that the proposed algorithm can solve job-oriented multi-objective FJSP efficiently and effectively.更多还原