Research On Scheduling Optimization Of Automated Warehouse Based On GA

Automated storage and retrieval system (AS/RS) is the outstanding result of the development of such modern logistics technology, storage technology and automation technology, which is the integration of storage, transportation and distribution. Automated storage and retrieval system is being applied more and more widely and composed of many subsystems. To improve the operation of automated warehouse efficiency, automated warehouse unloading scheduling optimization is important to study theoretical and engineering significance. Based on application and development requirements of modern logistics, this paper puts emphasis on the research of cellular optimization and path optimization in automatic high-rise warehouse used artificial intelligence theory-genetic algorithm.This paper introduces and analyzes the overall structure of AS/RS and the theory of genetic algorithm. Then it gives an emphasis on the problems of cellular optimization and path optimization. Goods allocation in the storage system is dynamic and uncertain, while allocation of goods delivered from storage is a certain complex operation mode. The process of AS/RS is as follows:firstly, optimizing the location of storage goods makes the efficiency of access to warehouse improved. The distribute way based on the random storage policy of dynamic storage allocation, and the multi-object function is built by the stability of storage, the turnover rate of storage goods and optimization target of the goods. The cellular optimization of AS/RS is proposed, which is a problem of multi-object optimization. Based on the optimization of storage location, this paper changes multi-objective problem into single objective problem by changing weight coefficient, which simplifies mathematical model of optimize storage goods. In the foundation of optimizing warehousing cargo allocation, path optimization has carried on. This paper is aimed at the characteristic the time spent by complex path shorter than single path. Partnershiping the goods put in and out the storage solves optimization problems of complex path operations according to single-parent genetic algorithms, making stacker running time to a minimum as the objective function to establish a mathematical model.Lastly, we obtain data information of different into and out goods. Then using MATLAB of GA emulates the cellular optimization and path optimization. Form the emulated pictures, it shows that the efficiency of access to warehouse is improved obviously, and stacker running time is much shorter than that before optimized. The result shows that optimization strategy based on GA can solve the problem of automated storage scheduling optimization effectively. In the end of paper, we summarize the work of this research, and propose the direction and goal of sequence research.