Hybrid genetic algorithm and simulated annealing approach for the optimization of process plans for prismatic parts

TLDR: In this paper, a hybrid GA and simulated annealing (SA) approach is used to solve the problem of process planning for a prismatic part in a dynamic workshop environment, where the activities of selecting machining resources, determining set-up plans and sequencing machining operations are simultaneously considered.

Abstract: For a CAPP system in a dynamic workshop environment, the activities of selecting machining resources, determining set-up plans and sequencing machining operations should be considered simultaneously to achieve the global lowest machining cost. Optimizing process plans for a prismatic part usually suffer from complex technological requirements and geometric relationships between features in the part. Here, process planning is modelled as a combinatorial optimization problem with constraints, and a hybrid genetic algorithm (GA) and simulated annealing (SA) approach has been developed to solve it. The evaluation criterion of machining cost comes from the combined strengths of machine costs, cutting tool costs, machine changes, tool changes and set-ups. The GA is carried out in the first stage to generate some initially good process plans. Based on a few selective plans with Hamming distances between each other, the SA algorithm is employed to search for alternative optimal or near-optimal process plans. In t...