A route planning strategy for the automatic garment cutter based on genetic algorithm

This paper proposes a route planning algorithm for the automatic garment cutter, a machine extensively used in the clothing industry, aiming at reducing the length and improving the smoothness of quick moving route for the cutter. With proper constraints for the cloth segments and knife-down points, the route planning problem is resolved into a generalized travelling salesman problem (GTSP) of the first category, for which an enhanced genetic algorithm is proposed. In this paper, we firstly outline the procedure of the algorithm and discuss some important details, including individual fitness calculation based on the multistage graph problem, a local search algorithm with 2-opt method, etc. Then a positionreservation crossover operator based on dual-relevancy, and an adaptive mutation operator based on population dispersion are proposed, which can accelerate convergence of the algorithm as well as prevent locking into local minima as much as possible. Finally, experimental tests are performed on the GTSP Instances Library and the data of garment CAD files, which demonstrates the effectiveness of our route planning strategy in terms of both solution quality and running time.