Abstract:In this paper, the multi-objective optimization problem was solved with the theory of information entropy and gray correlation analysis method in parallel. The objective function values were used to structure a data sequence. The multi-objective optimization was complicated by using data sequence relation model. Firstly, the grey relational coefficient and the entropy weight were calculated in parallel based on multi-objective value sequence. Then, the information entropy and the grey relational coefficient were combined and used to calculate the grey entropy parallel relational degree (GEPRD), that is, the grey entropy parallel analysis method was built. Finally, the GEPRD was used as the fitness value calculation strategy to guide the evolution of the heuristic algorithm. The Tri-objectives optimization model of job shop scheduling problem was established. In order to verify the feasibility of the new method ,the grey entropy parallel analysis method was testified with differential algorithm and genetic algorithm respectively to solve the Tri-objectives job shop scheduling problem. Experimental results show that this method is effective ,with this method, the convergent and uniform distribution of Pareto can be obtained by this two algorithms. Indicated that it was effective and reliable.The solutions obtained by the difference algorithm are better than those of genetic algorithm.