# Nesting Process Automation in the Footwear Industry: A Hybrid Approach to Minimize Material Waste
## 2 State of the Art
This is achieved through three interconnected neural networks: a geometry encoder, an interdependence analyzer, and a scrap predictor. Key contributions include a novel sustainable manufacturing practice for pre-nesting scrap prediction; a DL framework considering the geometry of the entire nesting set; a real-world data-derived baseline for comparison; and experimental validation on a private sheet metal dataset, demonstrating at least twice the accuracy and 16 times better data fit compared to the baseline, achieving a mean absolute percentage error of 24.8%. [PERSON] et al. [29] address the challenge of integrating complex nesting problems (CNPs) into hierarchical production planning. The proposed solution is a machine learning-based anticipation method for batch feasibility. A prediction framework evaluates various machine learning techniques, using newly designed feature vectors to represent CNP instances generated by a novel procedure (88,200 instances from 6000 shapes). An artificial neural network is identified as the most effective predictor, achieving 98.8% accuracy in batch feasibility decisions. This approach offers a practical way to incorporate complex nesting constraints into higher-level scheduling problems, improving efficiency and solution quality. A comprehensive review of the literature reveals that most studies concentrate on fitting both irregular and regular parts onto regular master parts. However, as previously noted, there is a lack of research specifically addressing the type of parts used in the assembly of moccasin and Strobel footwear, which are the focus of this study. An additional challenge of interest is the nesting of irregularly shaped parts onto irregular surfaces, such as leather, as shown in Figure 1.  
Leather contains varying quality areas, defects and even holes, all of which condition the positioning of parts. Figure 2 illustrates a nesting layout on leather with these characteristics. It can be observed that the area in red corresponds to the best quality of the leather, in which the most visible parts of the footwear are positioned, while the yellow area, being of a lower quality, is used for the rest of the pieces. The blue areas are discarded because of their low quality or because they are holes. This issue has been less extensively explored in the literature. Among the studies identified, a classification can be made distinguishing those that consider variations in leather quality from those that do not. Figure 1: Leather skin on a digitizing table. In the background, its digitization and the definition of its different quality zones can be seen. Figure 2: Example of nesting on a piece of leather with quality areas. Studies that do not consider leather quality include:
* [PERSON] et al. [30] developed a hybrid genetic algorithm with SA (Simulated Annealing) that introduces a mutation operator to facilitate faster convergence and improve result accuracy, leveraging SA's thorough exploration of the search space. * [PERSON] et al. [31] proposed a heuristic algorithm of quick location and movement (QLM) to solve the positioning of irregular shapes on multiple irregular sheets. The proposal includes two main parts: first, the approximation of irregular shapes to a polygon by means of a cluster of straight lines; second, the arrangement of the approximated shapes. * [PERSON] et al. [32] introduced a new nesting algorithm for leather manufacturing that employs a bottom-left heuristic strategy to position parts on the leather. Optimal placement sequence, including rotation, is determined through a SA based on a genetic algorithm. Other studies do consider leather quality, adding further variability to the problem:
* [PERSON] et al. [33] account for four quality levels and a fifth for holes, using a greedy algorithm to solve the problem. To simplify the process, the algorithm first reduces the master surface's polygons by calculating an outer and an inner polygon.