Research On Location Optimization And Picking Efficiency Of Intelligent Warehouse System

Under the background of the 14 th Five-Year plan,China pays more attention to the quality of economic development.Increasing investment in scientific and technological innovation and improving consumption level are the key means,which will inevitably lead to more frequent commodity circulation and trade exchanges in all walks of life.As the hub of logistics industry,the development direction of warehousing must be combined with emerging technology industries.The storage allocation mode and the optimization of goods picking efficiency are the key to improve the intelligent level of the warehouse.The reasonable storage allocation will greatly liberate the labor force and accelerate the production and circulation speed;a good picking path will shorten the walking time of the equipment and save energy consumption.The purpose of both is to make the system more flexible and the resource allocation more reasonable,which brings more economic benefits.Therefore,the research on these two aspects has strong practical significance.Based on the previous research,this paper continues to explore the space that can be improved,and carries on the mining research from the perspective of system model and optimization algorithm.The main work is as follows:Firstly,it discusses the background and significance of the research direction,summarizes the development of the research problems,and puts forward the problems worthy of further study.This paper introduces the overall structure of the intelligent warehousing system,the system operation level and the work flow of goods in and out of the warehouse,and expounds the connection between the non deterministic polynomial problem and this paper,which provides theoretical support for the later research.Secondly,aiming at the problem of irregular storage in the process of storage allocation,according to the actual allocation requirements,a combinatorial optimization model with three different objectives,i.e.the optimal storage time,the lowest overall center of gravity and the strongest product relevance,is designed.In the process of solving,in order to solve the disadvantage of genetic algorithm,dimension reduction coding is used to overcome the problem of invalid exchange of information,multi population cooperation is used to improve the local optimization problem,and mountain climbing algorithm is combined to further improve the optimization efficiency.The simulation analysis of the example shows that the model has the ability of dynamic adjustment through the specific data and three-dimensional view.In the algorithm comparison experiment,the mean value of the objective function and the convergence efficiency of the improved algorithm are better than the traditional algorithm,which proves the effectiveness of the algorithm and obtains better storage optimization effect.Then,aiming at the problem that the stacker has too long no-load running time when picking goods,the path optimization point is found by analyzing the composite operation process.In order to deal with the discrete path optimization problem caused by the combination of access queues,the traditional TSP(traveling salesman)problem is discretized,and the time optimization model in two-dimensional space is established to solve the optimal access queue.In the process of solving the problem,the original ant colony algorithm is modified,a discrete heuristic matrix is designed to deal with the discontinuous path scheduling,and an adaptive adjustment function is added to improve the pheromone retention problem caused by positive feedback.Through the simulation analysis of the picking scheduling problem,the feasibility of the model is verified through the algorithm comparison.By giving the optimized access combination path and algorithm optimization rate,the advantages of the improved algorithm in this problem are proved and the picking efficiency is effectively improved.Finally,the research results and innovation of the text are summarized,combined with the actual factors to explain the shortcomings of the theoretical research and the direction of improvement.Combined with the previous research results,this paper further optimizes the efficiency of the intelligent warehousing system,and achieves good experimental results,which not only provides an effective solution for the actual production process,but also provides a valuable reference for the research in the same field.