Study On Optimization Methods Of Order Picking Path In Automated Warehouse

In recent years, hardware equipment, automatic control and communication technology in automated warehouse tend to be perfect, but there is still insufficiency on management optimization and scheduling. Therefore, when ensuring accurate and timely completion of order picking task, how to optimize the order picking path is an important problem for automated warehouse managers. After having researched on a mass of information, we have a conclusion that almost all researches focus on improvement of genetic algorithm and ant colony optimization algorithm or combination of them about automated warehouse order picking path optimization, but there are less application researches on new algorithms such as particle swarm optimization algorithm, and less comparison researches on swarm intelligence algorithm. Referring to academic research data on swarm intelligence algorithm, we do some exploring researches on stacker's order picking path optimization methods in fixed shelf automated warehouse.1. Studying and analyzing the basic theory and operation process of swarm intelligence algorithm——genetic algorithm, ant colony optimization algorithm and particle swarm optimization algorithm.2. Studying on concrete operation technology of genetic algorithm, basic ant colony optimization algorithm and basic particle swarm optimization in solving order picking path optimization problem, and programming with Matlab to work out the minimum time cost and optimized route.3. To combine basic particle swarm optimization algorithm with genetic algorithm, at first, applying basic particle swarm optimization algorithm to produce initial population, and then applying genetic algorithm to optimize. Advancing particle swarm optimization algorithm based on simulated annealing to solve order picking path optimization problem.4. By comparing and analyzing the calculation results, coming to conclusions: when solving order picking path optimization problem, basic ant colony optimization algorithm converges fast and receives a best result; basic particle swarm optimization algorithm combining genetic algorithm has a better result than pure genetic algorithm and basic particle swarm optimization algorithm; and particle swarm optimization algorithm based on simulated annealing can get better result when the max iteration time is set appropriately, though particles'position changing is restrained. After balancing all algorithms'time and space complexity, virtues and shortcomings, solution effect, we may consider applying particle swarm optimization algorithm based on simulated annealing to solve order picking path optimization problem in reality.