Optimization of Sheet Material Layout in Industrial Production Using Genetic Algorithms

In industrial production, sheet material layout is a critical issue that directly impacts manufacturing efficiency and costs. Although various optimization methods exist, traditional layout algorithms often struggle to handle complex production requirements and constraints. Therefore, a more effective method is needed to address this problem. This study aims to explore the use of genetic algorithms (GA) to optimize sheet material layout, improving production efficiency and reducing costs. By combining the global search capabilities of genetic algorithms with the specific constraints of the sheet material layout problem, we aim to propose a more effective layout scheme. We have designed a sheet material layout optimization model based on genetic algorithms, transforming the sheet material layout problem into an optimization problem of a fitness function. The model considers sheet dimensions, shapes, quantities, and production equipment constraints, and optimizes the layout through genetic operations such as selection, crossover, and mutation. This paper focuses on introducing genetic algorithms into the layout of irregular sheet materials to reduce material waste during cutting and enhance production efficiency.