Design And Implementation Of Scheduling Strategy For Double Reach Stacker In Stereoscopic Warehouse

In recent years,with the development of the logistics industry,requirements of modern warehousing and logistics companies are higher being more concerned with efficiency of automated storage and retrieval system(ASRS).ASRS is a high automated management approach that could commands the stacker for task scheduling by issuing instructions.The scheduling problem of the stacker is the key issue for ASRS.The present scheduling path of ASRS often adopts direct assignment,which results in low operating efficiency,high time and energy consumption of the stacker.Good access path optimization of the stacker can improve efficiency in reducing storage costs,increasing the utilization rate and making guiding suggestions for the warehouse management process.To optimize the stacker’s scheduling strategy,this paper has conducted the following research:(1)By analyzing the working mode and movement characteristics of the stacker,a multi-objective optimization model based on running time,energy consumption and batch operation efficiency is proposed.(2)Learning the multi-objective optimization algorithms,it is found that in multi-objective optimization of low-dimensional solution space problems,NSGA-II shows certain advantages,but the phenomenon of solution set converge to the local optimal caused by early convergence and poor search ability are exist.(3)Based on NSGA-II optimization design for stacker scheduling problems,considering the actual scheduling situation,a double-layer coding sequence based on task type and cargo location order and excellent gene segment retention strategy are proposed.(4)Based on the excellent gene segment retention strategy,the cross mutation operation is improved,so that the excellent gene segments in the stacking machine scheduling coding sequence can be reserved for the next generation;at the same time,in order to adapt to the cross-mutation frequency requirements of each stage in the genetic process,using adaptive cross-mutation probability instead of the traditional fixedprobability,the probability of individuals cross-mutation at different stages is improved.(5)To optimize NSGA-II’s problems of insufficient search ability and trapped in local optimization,a local random search strategy based on the simulated annealing is proposed.(6)Comparing the improved algorithm(IMOGA)and the NSGA-II algorithm,it is found that the IMOGA solution sets has higher quality and faster convergence speed,and has higher applicability for stacker scheduling problems.After using IMOGA to get the pareto solution sets for stacker scheduling,the scheduling strategy is analyzed and designed,and a dynamic decision method based on the length of the stacker task queue is proposed to select the appropriate feasible solution from the solution sets as the final execution solution.(7)In order to facilitate the use of warehouse managers,the algorithm and scheduling strategy are embedded in the system,and a stacker operation scheduling system is implemented to display the operation information and adjust the stacker scheduling strategy,moreover the simulation and example analysis are carried out.