Workshop Layout Optimization Based on Genetic Algorithm with MATLAB Implementation

In manufacturing industries, workshop layout optimization represents a critical challenge. Optimizing workshop layouts can significantly enhance production efficiency by minimizing material handling distances and optimizing aisle corridor arrangements. Genetic algorithms serve as an effective methodology for solving such optimization problems. The MATLAB source code for genetic algorithm-based workshop layout optimization helps engineers better understand the underlying principles and methods of layout optimization. This source code provides a mathematical model and computational approach to optimize workshop layouts for maximum production efficiency. The implementation features chromosome representation of layout configurations, where each gene corresponds to equipment positioning parameters. The fitness function evaluates layout quality based on material flow distance, adjacency requirements, and operational constraints. Key genetic operators include tournament selection, crossover for exchanging layout sections, and mutation for introducing layout variations. Additionally, other optimization algorithms such as simulated annealing or particle swarm optimization can be integrated to further enhance layout solutions. The code structure allows for modular implementation of different optimization techniques, enabling comparative performance analysis. In conclusion, workshop layout optimization represents a promising field that can generate substantial benefits and profits for manufacturing operations. The MATLAB implementation demonstrates practical application of evolutionary algorithms to solve complex industrial layout problems with measurable efficiency improvements.