Study On Picking Task And Restocking Buffer Optimization Of Parallel Automated Picking System

As the shorter product lifecycle, the market with the characteristic of batch becoming gradually thinning and the trend of increasing commodity requirement including small batch, variety and shorter lead time leads to the gradual increase of picking cost in distribution center. To reduce the total cost of order picking effectively, the manual picking system was replaced by the automated picking system that is suitable for dealing with variety, small batch orders and works quickly, as a result, the picking time and labor intensity is reduced greatly, as well as the working efficiency and picking accuracy are increased greatly, hence more and more distribution centers of various trade have adopted the automated picking system.With the development of automated picking technology, in order to reduce the influence of each working process on picking efficiency and the coupling between picking process and replenishment process, the complex parallel automated picking system with the presorting function appears. This system takes advantage of time when single sorter is free to presort the next order. It will rapidly sort the commodity required by an order to the transport system.The average method of the each SKU picking quantiy is widely used by the parallel automated picking system, and this method is fast and effective, easy to implement, but a lot of waiting time appears to reduce the picking efficiency. The total equalization allocation method based on each distribution line is used in sorting scheduling policy, which increase the number of chanaging sorter SKU and greatly extend the total picking time. Therefore, studying parallel automated picking system is pretty meaningful for reducing total picking time, which can save logistics cost and promote service efficiency of the distribution center.However, the research on picking (sorting) field by domestic and overseas scholars mainly focuse on the artificial picking system while the picking mode between artificial picking system and automated picking system is greatly diverse. The research on automated picking system mainly concentrates on the transformation of sorter, selection and configuration optimization of picking equipment, while it rarely involves the studying of picking strategy and picking task allocation methods of optimization about automated picking system. In picking system of replenishment research mainly focuses on replenishment path optimization on the condition that the replenishment equipments are fixed, rarely on the design of optimization of relating equipments of automated replenishment system.Based on these, this paper deeply studies on the subproblem of automated picking system optimization including SKU splitting optimization, sorting scheduling policy optimization, restocking buffer optimization by the comprehensive utilization of order analysis, iterative optimization, clustering analysis and so on. During the researching process, the main content and achievement are below:(1) The modeling of parallel automated picking system.The mathematical optimization model of parallel automated picking system is established by analyzing its working flow. Firstly, the working flow of parallel automated picking system is described by virtual window theory and the picking time task model is established. Secondly, the mathematical model with respect to picking delay time of all picking machines and its quantity is established by analyzing picking delay time as one of the key factors which influence picking time deeply.(2) The SKU split optimizing of parallel automated picking system.In items of the SKU splitting, the more SKUs need to be splitted, the easier the efficiency of picking system improve while lead to increase the number of picking machines, and greatly increase the investment of distribution center. To promote picking efficiency on the condition of splitting less SKUs, it is needed to make the picking quantity of picking machine correspond the SKUs splitted. Firstly, the SKUs splitted by EIQ method before SKUs splitting optimization are determined. Secondly, according to the change of delay time before and after splitting SKU aiming at the SKUs splitted, three necessary conditions accordding with quantity of picking are put forward and proved. The SKU splitting optimization model based on delay time is established. Lastly, in view of the characteristics of the larger number of orders and the larger quantity of SKU splitted, this paper designs a Heuristic Adaptive Genetic Algorithm to solve the SKU split optimization model, and the simulation results of reducing about9%total picking time show that the optimization model and HAGA Algorithm of SKU splitting.is superior.(3) The sorting scheduling policy optimization of parallel automated picking systemThe Sorting scheduling policy optimization problem of picking system is tranformed to clustering problem and solved by designing a composite clustering algorithm in this paper. Firstly, it is put forward to cluster through choosing the distribution lines as the basic unit in accordance with the SKU structure similarity of orders based on the characteristics of the sorting scheduling. Here the step as belows: all the SKUs are described as vectors of multidimensional space. The space dimension is equal to the number of distribution lines. The vector in each dimension is equal to the transformed standard SKU picking quantity of the orders of distribution lines. Minimum variance clustering target model is established between each cluster. By use of the Euclidean distance, this paper translates the sorting scheduling policy optimization problem into order batching clustering problem. Secondly, basing upon the defect of hierarchical clustering method and k-means clustering method, the composite clustering algorithm based on hierarchy and partition is put forward, here the step as follows:the initial feasible solution is got by simplifying the hierarchical clustering method. The initial solution by using the improved dynamic clustering algorithm is optimized. It balances the relationship between expanding the search space and improving the velocity of solution very well basing on the division. Lastly, the superiority of clustering target model basing on minimum variance and composite clustering algorithm is proved by using examples.(4) The restocking buffer length optimization of automated restocking system.In terms of the automated restocking system corresponding with the automated picking system, if the restocking buffer length is too short, it will influence the efficiency of picking system, while if the restocking buffer length is too long, it will increase the total investment of equipments. The optimization of restocking buffer design is studied in this paper. First of all, according to the overall analysis to working flow of the automated restocking buffer in the automated picking system, a balanced mathematical model of the commodity on conveying equipment input and output is established, as well as the optimal condition and critical condition when restocking buffer length contenting the picking task are put forward and proved. Secondly, according to the layout relationship between the restocking system and the picking system, the mathematical models reflecting the process of the arrival of the commodity and the restocking buffer length optimization is established. Lastly, the heuristic clustering method of "first clustering, ranking after" is designed on the condition that it makes the difference of average outbound time of two SKUs basing on mission sequence as the correlation of the two SKUs, then, preparation system SKU distribution optimization that can reduce the arrival time of commodity from restocking system to shorten the restocking buffer.In order to validate the proposed method and the high efficiency of the algorithm, this paper takes the waterfall automated picking system designed for the tobacco industry by a logistics technology Co.LTD. as the research object. The algorithms and models are confirmed by some examples based on the real customer order data of multiple regions's cigarette logistics distribution center. The calculation results show that the proposed algorithm and the optimization model can solve not only the automated picking system task optimization of mass orders but also the design of restocking buffer length optimization problems very well.