Optimization of process route by Genetic Algorithms

Process route sequencing is considered as the key technology for computer aided process planning (CAPP) and is very complex and difficult. In this paper, based on the analyzing of various constraints in process route sequencing and the astringency of Genetic Algorithms (GAs), the GA is reconstructed, including the establishing of the coding strategy, the evaluation operator and the fitness function. The new GAs can meet the requirement of sequencing work and can meet the requirement of astringency. The natural number is adopted in coding strategy, the “elitist model” and the “tournament selection” are adopted as selection operators, the nonconforming sequential searching crossover operator is used and the inconsistent mutation operator is adopted, the fitness function is defined as a formula of the sum of compulsive constraints with each weighing, and these constraints are used as the control strategy for GAs in the searching process. By using GAs in the optimization, the optimal or near-optimal process route is obtained finally.