Research On Modeling And Optimization Of Scheduling Based On Multi-level Shuttle For "Parts-to-person" Warehousing System

With the implementation of the national intelligent manufacturing development strategy,the transformation and upgrading of traditional manufacturing industries is speeding up,and at the same time,it is developing towards digitalization,networking and intelligence.The storage system is an important part of the manufacturing supply chain,in order to improve the efficiency of warehousing,reduce the cost of scheduling,improve the response-ability of material demand,optimize the service quality of storage,to upgrade the Customer satisfaction of the shuttle storage system,which is suitable for the "multi-variety,small-batch,multi-batch and high time-efficiency" manufacturing mode,in order to solve the scheduling problem of goods in and out of the warehouse,this paper studies the task scheduling problem of the shuttle storage system under the compound operation mode.The main research contents of this paper are as follows:Firstly,taking the shuttle storage system as the research object,this paper introduces the structure and operation flow of the shuttle storage system,expounds on the operation flow of the storage system,and analyzes the different operation modes,the difference between single operation mode and compound operation mode is given.Secondly,according to the single operation mode and the compound operation mode,the traveling distance and operation time of the shuttle and the hoist are analyzed and calculated,taking the shortest running time as the objective of optimization,an optimization model of the composite job scheduling for in-and-out storage is established.Then,based on the standard particle swarm optimization algorithm,an improved particle swarm optimization algorithm is proposed.Combining with the dynamic inertia weight,the particle velocity is dynamically updated to enhance the global and local search ability of the algorithm,the variable neighborhood search strategy and Metropolis rule are added during the iteration of the algorithm to avoid premature convergence and improve the quality of the optimal solution.Finally,taking a batch of order storage tasks as an example,we compare the scheduling time before and after the optimization.The results show that the proposed scheduling optimization model can effectively shorten the total time of storage,improve the efficiency of task scheduling;The improved particle swarm optimization algorithm is compared with standard particle swarm optimization algorithm and tabu search algorithm under different task size data,the results show that the improved particle swarm optimization algorithm is more effective and reliable in solving the scheduling problem of shuttle vehicle storage system.