Real-time optimum sequencing of wood-cutting process

An optimization algorithm was developed to minimize waste in a real-time cutting process of high-volume solid wood furniture production where each wood strip is cut into shorter pieces depending on the production order. The optimization problem at hand is an extension of the standard one-dimensional cutting stock problem with random variable stock size. In addition, each stock has random quality variation along its length, which is unknown before the production of the stock. The defect-sensitive stock is produced online and real-time decisions are necessary to sequence the cuts. The proposed optimization algorithm for this dynamic problem has two main sub-algorithms: adaptive fuzzy and recursive solution. The adaptive fuzzy sub-algorithm prioritizes the needed items to be cut based on the required length and quantity. The recursive sub-algorithm, as a depth-first search algorithm, creates the permissible cut patterns for each strip. The experiments with the optimization algorithm illustrate promising results and the waste data obtained were well below the current figures.