Under The Environment Of Supply Chain Manufacturing-Distribution Secondary Inventory Optimization Research

With the globalization of economy, in the past the competition among individual enterprises change into the competition between supply chains, Inventory control, as one of the important content of supply chain management, its quality directly influence the realization of agile supply chain and the realization of the value chain value-added, thus strengthening the inventory control in supply chain environment is very necessary. The traditional inventory management consider only from their own point of view, a single enterprise achieves its own inventory optimization, and under the environment of supply chain inventory control is the optimization of all the resources in the global supply chain, based on supply chain inventory optimization goal, through to each node enterprise on the supply chain inventory reasonable allocation, maximize the whole supply chain profit.In this thesis, considering for the supply chain system cost optimization and turnaround time, through the analysis of the basic theory of multi-stage supply chain inventory and the structure, the optimization goal, ordering strategy of production distribution secondary inventory system, build the secondary inventory system from manufacturers to distributors, analysis of cost and turnaround time of manufacturers and distributors. Studies have shown that the system overall inventory function and turnaround time function, when the manufacturer adopts cyclical (s. S) replenishment strategy, distributor adopts continuity (R, Q) replenishment strategy. Then, through comparative analysis of the inventory model, choose the more optimization of the immune genetic algorithm to solve the model, and the design of immune genetic algorithm. Taking an example of LR20 of Dongguan Juda electronic co. LTD. combine with the building model and the algorithm design in this thesis, using MTLAB software simulation to solve. Through the analysis of the results, verify demonstration and feasibility of the model constructed in this paper and the algorithm.