Research On The Optimization Algorithm Improvement For Path Selection In Automated Warehouse And Solution Effect Comparison

With the scale of enterprise modernization production expand and deepen, storage has become an important and indispensable component of production logistics system. In recent years, hardware equipment, automatic control and communication technology in automatic warehouse tend to be perfect, but there are still insufficient on management optimization and scheduling. so on ensure accurate and timely completion the selection tasks, how to optimize the selection of path is an important problem confronted with the selection tasks in automatic warehouse.This paper do some research exploringly aiming at stacker path-choosen optimization methods in fixed shelf automatic warehouse. First of all, analyzing the simulated annealing algorithm and ant colony algorithm which can solve the TSP problem. Secondly, applied the simulated annealing algorithm and ant colony algorithm to stacker path-choosen optimization in fixed shelf automatic warehouse, using Matlab software to solve the TSP problem on goods-choosen of stacker. Again, aming at the fact that ant colony algorithm is easy tend to local optimal, proposed a method that combine the ant colony algorithm with local optimal search strategies (3-opt method) , and restrict some ants's contribution to pheromone track by setting the upper limits and lower limits of pheromone concentration, making the search have the opportunity to jump out of local extremum points. The improved ant colony algorithm is greatly improveing convergence rate and enhancing the global optimization ability, and the performance of solving the optimization problem has been greatly improved. The experimental results gained by using TSPLIB data show that the improved ant colony algorithm has a better ability on finding the optimal solution for large and medium-sized TSP problem. In addition to to solve automatic warehouse path-choosen optimization problem, the improved ant colony algorithm also can solve a series of mechanical systems optimization problems and some linear or nonlinear optimization problems, it also needs to be improved and further study.