Study On The Slotting Optimization In Bearing Marketing Inventory

In order to manage the warehouse more effectively, and improve the whole operating efficiency, it required to optimize the allocation of storage location assignment, to reduce the loss of goods in the warehouse, then improve the customer satisfaction for goods quality; reducing the cost of cargo handling and storage, finally achieve the goal of reducing the logistics cost and improve economic performance.This thesis took unit goods format warehouse of a certain bearing marketing company for background. On the basis of summarize the related theory about warehouse home and abroad, the thesis does a further research of storage location assignment.According to the type and application of warehouse in bearing distribution enterprise, this thesis introduced the method of storage location numbering based on distances, using the way of random classification storage.The paper focused on the storage location assignment problem of in/out warehouse of unit goods format. For the increasing of costs in bearing warehouse handling and storage, we give up the storage location strategy which goal is efficiency, and our goal is the shortest distance of handling equipment. To address the issue of storage location assignment problem, we mainly consider the static situation, which is equal to assign storage location for one baring out of warehouse and without considering the other, or assign storage location for one baring in warehouse without considering the other plan out of warehouse, we propose a storage location assignment model which consider one in-warehouse plan and some out-warehouse plans, in order to maintain the shelves firmly, we establish a model of minimum focus shelves; then in order to solve the bearing quality problems, we use the slotting optimization strategy which constraint is bearing quality satisfaction of out warehouse, and ensure the bearing quality to achieve a certain satisfaction. For the existing optimization strategy, because of its transport equipment load ability is limited although it is simplified to TSP, we add the constraint of the loading capacity into storage location optimization model.Use Genetic Algorithms to solve the slotting optimization problem. Before using Genetic Algorithms, we switch the multi-objective problem into single objective problem, and then use the sequence encoding strategy, divide a chromosome into two parts: one part corresponds to the in-warehouse plan, the other corresponds to the out-warehouse plans. After crossing and mutating, we repair the infeasible chromosome. Setting different weight coefficient, we analysis the impact of weight coefficient to slotting optimization results. And set different bearing quality satisfaction, analysis whether the optimization result is impacted. Then set different loading capacities, compare the impact to optimization result. Finally, we design the experiments compared with storage location optimization without considering loading capacities. We have verified the validity of the model though these experiments, and have achieved the desired results.