An Effective Hybrid Genetic Simulated Annealing Algorithm for Process Planning Problem

Process planning is an essential part for a Computer Aided Process Planning (CAPP) system in the dynamic workshop environment. It is a combinatorial optimization problem to conduct operations selection and operations sequencing simultaneously with various constraints deriving from practical workshop environment as well as the part to be processed. In this paper, a hybrid genetic simulated annealing algorithm has been developed, which combined the strengths of genetic algorithm (GA) and simulated annealing (SA), to solve this problem. The GA is carried out as a main frame of this hybrid algorithm while SA is used as a local search strategy to help GA jump out of local optima. A case study is employed to verify the performance and efficiency of the hybrid genetic simulated annealing algorithm (GASA) and the experiment results show that the developed hybrid GASA can generate satisfactory solutions.