Optimisation of Steel Plate Cutting Paths Based on Generalised Traveller Model and Hybrid Greedy Heuristic Algorithm

In this paper, aiming to reduce the empty travelling distance in steel plate cutting process, a hybrid greedy based heuristic algorithm, combined with simulated annealing algorithm and genetic algorithm, is proposed for optimising the process path of steel plate cutting through Python program. Firstly, Dijkstra's algorithm and greedy algorithm are used to optimise the paths for simple cutting layouts and successfully reduce the empty journey length. Subsequently, the simulated annealing algorithm is applied to complex cutting layouts such as internal efficient cutting routes and complex outer boundary shapes, which significantly optimises the cutting efficiency. Further considering the case of cutting multiple rectangular pieces inside an ellipse, a genetic optimisation algorithm is applied to effectively solve the complex constraints of symmetric distribution and cutting order. Finally, a nonlinear objective function and Dijkstra's algorithm are designed to optimise the paths for cutting four small rectangular parts inside the ellipse, and the optimal position and number of "bridges" are determined, which further improves the productivity and cost-effectiveness. Through these methods and experimental results, the significant impact of path optimisation on productivity in industrial applications is verified.