Research On Scheduling Optimization Method Of AS/RS

The automated warehouse is the core of logistics systems.Optimizing its scheduling is beneficial for improving warehouse management efficiency,reducing the workload of transportation equipment,and saving economic costs for enterprises,which has important engineering practical significance.Due to the increase in the total amount of goods in stock and the turnover of goods,the current automated warehouse of enterprises had the following problems: on the one hand,disorderly storage of goods led to the high center of gravity and excessive deformation of the shelf,and the distance between goods and the inbound and outbound platforms was irregular and closely related.The distance between goods and the inbound and outbound platforms was relatively far,which increased the additional workload of inbound and outbound operations for transfer equipments.On the other hand,when there were many goods entering and exiting the warehouse at once,the running path of the stacker was not the shortest,which increased the running distance of the stacker and increased the operating cost.In response to the above two issues,this article analyzed and established mathematical models for the optimization of storage locations and stacker crane paths in automated warehouses.It improved the traditional adaptive genetic algorithm(AGA)used in the current warehouse scheduling system,and used the improved algorithm to solve the model to obtain the best optimization solution.The main research content was as follows:(1)Analyzed the principle of cargo location optimization and the working mode of stacker,determined the optimization goal and established the mathematical model.In-depth understanding of cargo location optimization principles and common ways,analyzed the stacking machine compound operation,single lane picking operation and multi-lane picking operation principle.Taking the goods with the lowest center of gravity,the shortest distance between the goods and the entry and exit platforms,and the close storage of the goods with strong correlation as the optimization objectives,the mathematical models of the goods location optimization were established.The coefficient weight method was used to transform the multi-objective optimization model into the single-objective optimization model.Aiming at the shortest operation time of the combined operation of the stacker,the objective function was established by the method of location matching,and aiming at the shortest operation path of the single and multi-lane picking operation of the stacker,the bin-packing constraint was introduced to create the objective function.(2)Improved the current algorithm optimization based on the stereo warehouse scheduling optimization problem.On the one hand,the average parameter and constant term of fitness were introduced into AGA’s formula of probability of crossover and mutation,so that the probability of crossover and mutation of the current optimal individual was not 0,the improved adaptive genetic algorithm was called IAGA,which could solve the problem that the AGA was stagnant in the early stage of evolution.On the other hand,IAGA and simulated annealing algorithm(SA)were combined into adaptive simulated annealing genetic algorithm(ASAGA)to improve the local search ability of the algorithm.(3)Carried out the simulation analysis of the scheduling optimization to set up the working condition of the optimization of cargo location and the optimization of stacker’s routes.Set the actual working condition for the optimization problem of the cargo position,the compound operation of the stacker and the picking operation.The results showed that ASAGA could improve the precision of AGA and effectively solved the problems of high center of gravity and excessive deformation of the shelf,the operation distance of the stacker was reduced,the management efficiency of the warehouse was improved and the operation cost was reduced.