A Genetic Crow Search Algorithm for Optimization of Operation Sequencing in Process Planning

Computer-aided process planning represents the main link between computer-aided design and manufacturing. One of crucial tasks in is an operation sequencing problem. In order to find optimal plan, problem formulated as NP hard combinatorial To solve this problem, a novel genetic crow search approach (GCSA) proposed paper. The traditional CSA improved by employing strategies such tournament selection, three-string crossover, shift resource mutation. Moreover, adaptive crossover mutation probability coefficients were introduced improve local global abilities GCSA. Operation precedence graph adopted represent relationships among features vector representation used manipulate data Matlab environment. A new nearest mechanism strategy added ensure that elements machines, tools tool direction (TAD) vectors are integer values. Repair handle constraints after initialization steps. Minimization total production cost optimization criterion evaluate plans. verify performance GCSA, two case studies with different dimensions carried out comparisons some modern algorithms from literature discussed. results show GCSA performs well for planning.