MLP Based on Dissimilarity Features: An Application to Wood Sawing Simulator Metamodeling

Short and mid-term production planning and control in the sawmill industry are complicated by several sources of uncertainties. Consequently, it is difficult to predict in advance what set of lumber would be obtained from a specific log. Even if sawmill simulators that can simulate the sawing of a log from a scan of its profile exist, they can be extremely computationally intensive. Several alternative methods, based on machine learning algorithms using different sets of log descriptors were explored in previous works. This paper proposes the usage of multi-layer perceptrons, as well as a vector of features based on the pairwise dissimilarities from the log scans to a set of selected representative logs, chosen as the class medoids. Several MLP architectures are tested and compared on two different datasets with a previously proposed k-Nearest Neighbors algorithm to validate the performance of the proposed set of medoid-based features. While the best-performing architecture depends on the dataset considered, all MLP models demonstrate lower RMSE than the baselines.