Study on shop layout based on SLP and improved genetic algorithm

To address low logistics efficiency in Workshop A, this study proposes a collaborative optimization method integrating Systematic Layout Planning (SLP) and an Improved Genetic Algorithm (IGA)^[1]. While traditional SLP has static limitations under complex constraints, the new approach converts SLP’s logistics relationship matrix into IGA’s initial population rules and introduces dual optimization mechanisms: dynamic crossover rate and hierarchical mutation rate. Simulation results show a 17.3% reduction in material transportation volume, effectively resolving multi-objective conflicts and local optima issues. This research provides a theoretical reference for intelligent manufacturing layout optimization.