面向工件的多目标柔性Job Shop调度问题研究

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。

Proposing an Integrated Genetic Algorithm for Solving Job-Oriented Multi-Objective FJSP

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.