Optimization of Machining Operation Sequence Problem using an Integrated Genetic and Simulated Annealing Algorithm

Among the most important and difficult tasks in CAPP (computer-aided process planning) is optimizing the machining operation sequence, which calls for a lot of iterations and a significant amount of computational time. Decreasing the frequency of tool changes and machine setup iterations is the primary goal of improving the machine operation sequence. To solve the CAPP problem and determine the shortest manufacturing time, this study suggests a hybrid approach called the integrated genetic and simulated annealing (IGSA) algorithm. The best sequence with the shortest manufacturing time, discovered in the first algorithm (genetic), has been proposed to be the starting sequence of the second method (simulated annealing), which searches for neighbouring ideal sequences. The feasible solutions for a given issue are the best sequences that can be found using this integration technique. The performance of the suggested IGSA algorithm was tested, including various benchmark problems with simple precedence constraints. The comparisons of benchmark and case study results with other existing algorithms in the literature show a drastic improvement in the computational time and optimal solutions.