Optimization Based On Genetic Algorithm Automated Warehouse Goods

The traditional warehouse management usually places goods at the nearest available area when the goods arrive. It comes from the past experience only consider less dynamic changes in commodity demand and changes in customer demand patterns. Traditional goods layout causes complex inefficient processes and lack of space utilization. Therefore, the slotting optimization has been produced.The slotting optimization management determines an appropriate location routing by goods attribute. Based on location routing strategy, depot management offers better swifter and considerate service though the same labor or cost.Based on the research about The Mengniu AS/RS, this thesis carries out a method to put the goods in the AS/RS into the optimal slots.First of all, on the basis of analyzing logistics management, this thesis puts forward the slotting optimization concept. Combining the storage strategy with the slotting assign principle, configuring a slotting optimization model to enhance the warehouse goods turnover rate and improve the whole stability. Secondly, establish multi-objective genetic algorithm to realize optimization by analyzing the existing slotting optimization methods. It tries to make use of compromise approach to solve the optimization model. Finally, research the artificial results of optimizing model by MATLAB genetic algorithm toolbox and analyze the simulation results.