Design of an improved intelligent optimization logistics path planning model for the background of the new industrial era

ABSTRACT In the context of the new industrial era, intelligent manufacturing, intelligent factories, and intelligent logistics have become hot topics during the industrial revolution. In order to meet the flexibility, flexibility, and efficiency of factory logistics in the era of Industry 4.0, and improve the punctuality of logistics distribution, a factory logistics path planning model is designed based on grid environment and Ant colony optimization algorithms. The algorithm optimizes and improves the search ability and adaptability of Ant colony optimization algorithms. The test results show that the Pheromone volatile number is 0.3, which is a moderate value. When the importance of Pheromone is 3 and the heuristic factor is 6, the average optimal cost and the average number of iterations are the minimum. As the variable increases, both evaluation indicators show a trend of decreasing first and then increasing. The improvement of the initial Pheromone can speed up the Rate of convergence of the algorithm, while the quality of the planned path results is better than that of the traditional planned path, and the shortest path length is reduced from 26.46 to 24.38. The path smoothed by B-spline curve is smooth and continuous. The algorithm proposed by the research institute converges quickly, with the shortest path value being 59.43, and the maximum reduction in path length reaching 18.44%. The optimized path length converges quickly after fluctuations, and the algorithm has strong adaptability to the environment. This model can achieve accurate logistics path planning, provide competitive solutions for the implementation of intelligent logistics in factories, and promote the 4.0 factory to build a truly intelligent factory. Graphical abstract ACO was first used to solve the Traveling Salesman Problem (TSP). Assuming there are cities on the plane, defined as, the connection between cities constitutes a combination, and the cost measure of the connection between cities is expressed as. The core of TSPE is to find the path back to the starting point after traveling through all city nodes, while ensuring the lowest cost measurement.