Automated Warehouse Goods Allocation And Stacker Path Optimization Research

As an outstanding product of the development of modern logistics technology and automation technology advances, automated warehouse is an important part of logistics system, which includes storage, transport and distribution. Automated warehouse is a complex automated system which scheduling problem affects the efficiency of the system directly. In order to manage the automated warehouse effectively and improve overall operational efficiency, the thesis has optimized the goods allocation and the stacker picking path, thus decreasing goods handling capacity the time wasted during storing process and improving the enterprise benefits. So researches on the scheduling problems of automated warehouse have important theoretical significance and practical application value. Based on summing up researches on the automated warehouse scheduling problem, I have studied the goods allocation and the stacker picking path optimization problem. In the paper, the main works are described as follows:The automated warehouse is elaborated, including of the concept, advantages, disadvantages and the domestic and foreign development situation. Its system structure has been analyzed. Finally, calculation theory related to automated warehouse is expounded and lay foundation for subsequent research. Genetic algorithm concept is introduced and its advantages and disadvantages are discussed.Automated warehouse slotting optimization principle is analyzed. To ensure the fixed shelf stability and improve access efficiency, the slotting optimization model is established. To the specificity slotting optimization, genetic algorithm is designed for the model. The selection and crossover are improved. Finally, the results of the slotting optimization are simulated by MATLAB.Stacker order picking path optimization problem is solved, which is similar to Traveling Salesman Problem. Traveling Salesman Problem is NP-hard problem which the solution quantity will increase exponentially with the number of cities. The stacker running path is the same. To solve this problem, the mathematical model of stacker running path is established. An improved genetic algorithm is proposed. At last, the optimal solution of stacker picking path is obtained. The simulation results show that the proposed method is effective.