Abstract: It is difficult to find the global optimal solution for the multi-objective job-shop scheduling problem and the solutions obtained usually belong to the Pareto optimal ones. A multi-objective scheduling optimization model is given for the Job-shop-like knowledgeable manufacturing cell and the relationship between the multi objectives is analyzed. The properties of key arcs of tasks are presented through the analysis of its disjunctive graph and the conclusion is obtained that it is helpless to improve the function value by changing the direction of the middle key arc of job. A simplified neighborhood is proposed based on the conclusion which can reduce greatly the number of feasible solutions to be searched. A self-evolution algorithm for multi-objective scheduling problem is proposed based on the properties of the simplified neighborhood by the use of adaptive heuristic critic method whose associate search module can find the best action for the concurrent solution to obtain a better solution by learning and training, and such ability of the module will be improved continuously with the training increasing. The numerical simulation results show that the algorithm proposed has the excellent ability to search the optimal solution for the proposed scheduling problem and possesses obvious evolution capacities through learning.

Key words: self-evolution;multi-objective scheduling;knowledgeable manufacturing cell;Job-shop