| Generally speaking, most manufacturing enterprises are accustomed to classify goods, especially the manufacturing enterprises with multi storage and multi goods are willing to do this. But this will cause a certain impact in the future delivery. Because the customer’s order may contain the multi types of goods, the enterprise’s stocking vehicle has to travel between multiple warehouses, especially when a car loads more than one order goods, the stocking vehicle consider the delivery sequence and will cause the vehicle routes cross in loading process. The inventory allocation strategy is adjusted according to the law of demand from the assembly line. It can reduce the route duplication phenomenon and improve the stocking efficiency. So it can improve the distribution efficiency.Firstly, the elements of the storage system and the operation flow are analyzed. In particular, the existing inventory allocation strategy and demand correlation is described. It can find the factors that affect inventory allocation.Secondly, the investigation period is long enough, and it can represent the demand of this period time. The distribution of goods in the warehouse is in accordance with the law of demand. Based on this rule, demand correlation is defined and quantified. With the goods in different storage to meet the requirements of the laws and the correlation to reduce the order distance two stage object function, it builds inventory allocation optimization model with storage capacity and order constraints.Finally, for the model, the first stage uses genetic algorithm to solve, and some modification to the standard genetic algorithm is made, and this paper uses genetic algorithm toolbox. The second stage uses the heuristic algorithm to solve. Through the concrete examples, the distribution quantity of kind of goods in different warehouses is obtained. The results are compared with the optimization, and it can be proved that the model can reduce distance of the vehicle. |
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