The application relates to a
layout optimization and order
batch method based on square piece features and a Pearson
correlation coefficient, and relates to a
layout optimization and order
batch method applied to the field of intelligent manufacturing. The application solves the optimal
cutting problem of square pieces in current individualized industrial products. The steps of the application are as follows: 1. determining a similar condition, establishing a one-dimensional array of required materials for each order; 2. applying a Pearson
correlation coefficient to determine the similarity of each order, and combining similar orders into a batch; 3. in the same batch,
cutting by material, and preprocessing the square piece data of the same material; 4. applying a large product item
cutting method with the original sheet width as the resolution reference to start cutting; and 5. applying a small product item dense paving method to arrange the remaining small product items. The application fully utilizes order information and product information, combines with the actual production, and proposes a two-stage cutting method, effectively improves the plate utilization, and is suitable for batch cutting of large-quantity and multi-type individualized customized square pieces.