One-dimensional cutting stock problem (1DCSP) is a problem mainly applied in the pipes, cables, and paper rolls industries, and it is a problem of minimizing the trim loss of the stock while satisfying the demand of orders. 1DCSP can be solved by the integer linear programming to get an optimal solution. However, the computation time is exponentially increased depending on the number of types of orders and its quantity demanded. Although many heuristic methods have been proposed to solve the problem, it is difficult to develop a heuristic method that always provides a good solution to various problems due to the performance that is highly dependent on problem domain. In this paper, we propose a method to generate observations by producing various 1DCSPs, and then use the artificial neural network (ANN) algorithm to select the heuristic method that provides a good near-optimal solution for any 1DCSP.
<br>ANN models were implemented using the Sequential module of TensorFlow 2.0 Keras. According to the experimental results, the minimum value of root mean square error (RMSE), mean absolute error (MAE), accuracy, precision and recall are found with specific combination of parameters of batch size, epoch number and optimizer.
