Research on optimization of workshop material distribution path based on genetic algorithm

This study proposes an optimization method based on an improved genetic algorithm for the optimization of workshop material distribution path, and verifies its effectiveness through theoretical design and simulation experiments. The study first analyzes the multi-constraint characteristics of workshop distribution path optimization, and makes adaptive improvements to the standard genetic algorithm, including heuristic generation of the initial population, dynamic fitness function design, and optimization of genetic operators, to improve the global search ability and convergence speed of the algorithm. Based on the permutation encoding method and the path length calculation model, a solution framework suitable for closed distribution paths is constructed. Simulation experiments show that the improved genetic algorithm is superior to the standard genetic algorithm in both path length and computational efficiency, with an average path length reduction of about 15%, a convergence speed increase of about 30%, and an average computational time of only 12.5 seconds. The research results confirm the application potential of this method in optimizing workshop material distribution paths and improving logistics efficiency, and provide theoretical support and practical reference for path planning in intelligent manufacturing.