Papers by Keyword: Constraint Matrix

Abstract: The self-optimization method for power generation proposed in this paper is an initial allocation of power energy. Every unit is according to the same principles of utilization hours in the same province. Under the premise of unchanged total power of each power generation unit and the security of the power system as well as the discharge standards, this method can achieve the minimum social resource consumption and the minimum discharge meet the demand of power energy at the same time by adjusting of autonomy optimization among internal different units. It is an important means and effective way to carry out energy generation scheduling work.

Abstract: Through deep analysis of the solvability, which is based on interval linear equations and inequalities systems, for a given optimal solution to interval linear programming problem, we propose the construction method of constraint matrices corresponded by the optimal solution in this paper.

Abstract: Currently, neither the efficiency nor the effectiveness is sufficient in the area of the assemble optimization that commonly involves the genetic algorithm. A novel method to solve the cumbersome problem in the optimization of assembly sequences was proposed. On the basis of the assembly constraint matrix, the optimized assembly sequence is obtained with the proposed evaluating factors of the process requirement. That is the evolution of the original genetic algorithm to a certain extent. The effectiveness of the proposed method was proved by the comparison with the ant colony algorithm.

Abstract: Operation sequencing is one of the most important tasks in process planning. The sequencing procedures associate manufacturing features from 3D CAD models and machining methods together to satisfy certain manufacturing process constraints. In order to simplify process constraint aggregations, two types of constraint matrixes, feature constraint matrix and the operation constraint matrix, are proposed in this paper, which take into account of the compulsive constraints, such as geometric topology constraints, manufacturing process knowledge criteria, custom compulsive constraints and so forth. Accordingly, an iterative genetic algorithm is proposed, which is naturally used in the manufacturing feature level and operation level. In the manufacturing feature level, feasible feature sequences are generated based on the analysis of feature constraint matrix. In the operation level, the information that is contained in the machining operation such as machine tools, set-ups and cutting tools is considered to optimize the operation sequences based on the results acquired in the feature level. Compared with the traditional simple genetic algorithm, the iterative genetic algorithm is proved to be superior in shortening the operation sequencing time.