Research For Stock Dynamic Layout Optimization Based On Scattered Picking Areas In Garment E-commerce

As for B2 C model of garment storage central,the scattered picking areas is customized to it.and,the trading volume of garment in E-commerce has been increasing approximately 20% in recent years and this ordering model with small quantity enlarged the scattered picking areas gradually.So,this paper focus on stock dynamic layout optimization on scattered areas in garment E-commerce,which can always accelerate the turnover rate and improve the warehouse's service level.Nowadays,so many scholars concern on how to heighten the efficiency on in/out stock and the stability of storage rack.But,this paper mainly aims to the garment layout problem with different seasons in a multi-stage storage in E-commerce sale model.The significance of this paper is expected to explore a scheme for warehouse system,especially for seasonal commodity,like garment or fresh food.At the same time,it also can be a direction for company's warehouse operation and advance the national operation of warehouse.In totally,it can decrease the cost of warehouse management significantly.Firstly,this paper proposes a method to optimize the garment slots when it comes to putting new products into storage rack.At the very beginning of each season,manager should program the multi-stage garment layout problem in scattered picking areas.According to corresponding periods ordering data,this paper proposes a single season with multi-stage stock assignment model,which also can be called class formulation assignment model(CFAM).This paper using CIO index to build CFAM,which makes the stock of different products with multi-stages came true.At last,this way changes hot products into convenient location and save the room in the warehouse.Secondly,making sequence relationship analysis on batching picking lists for quadratic stock assignment optimized problem between two different seasons.As we all known,when the present garments stock decrease to 25% of total seasonal quantity,the manager should make planning to optimize the present layout for the next season.At first,this paper analyzed the batching picking list in previous stage by sequence algorithm,and get the frequency items values of different categories of garment.Then,it uses the distance information for QSA model.In the end,this model realizes that relationship items are putted together and saving the room in the same time.Thirdly,when it comes to the experience of solving models,the first part uses greedy algorithm to building searching tree to get optimal categories storage layout.The second model was solved by hybrid Genetic algorithm,which takes a greedy strategy into achieving better heuristic initial solutions before,and uses GA algorithm to realize it.In the end,this paper shows optimal permutations after solving.Meanwhile,this paper used double programming to realize the algorithm.Finally,I make conclusion about this paper's achievements.Whether it is a single-season first-time storage allocation optimization,or different seasons storage layout space optimization,the total cost of picking is significantly reduced,and the storage space is optimized.At last,the cost of warehouse operation is maximally saved.The results are significant.The rationality of the design of the step of the hybrid genetic algorithm is proved.By cycling the different hyper parameters,the effectiveness of the parameter design of the hybrid genetic algorithm is sure.