Study On Modeling And Optimizing Of Supply Chain Planning Under Uncertainty Environment

With the improvement of science and technology and the formation of market globalization, enterprise faces on greater opportunities and challenges. In order to adapt new economic and market competition, the enterprise management change from the traditional vertical integration into the crosswise integration, as while appearing supply chain management. The structure of the supply chain which includes various uncertainties has great influence on the construction and operation of supply chain. Modeling and optimizing of supply chain planning under uncertainty which is studied has important theoretical significance and application value.This paper considers various uncertainty factors, and analyses multi-period supply chain integration plan under the uncertain environment. The research content is as follows:Firstly, under the condition that both the customer’s demand and supplier’s supply price are uncertain, we study two cases respectively, including certain production capacity and uncertain production capacity of manufacture. We establish stochastic expected value model, and optimize this issue using the Genetic Algorithm which can examine constraints through stochastic simulation. Evaluate and compare the results of two cases, we can conclude that: uncertain of production capacity can increase total cost of supply chain.Secondly, we study multi-period integrated supply chain planning, under uncertainty demand and uncertain price of suppliers. Formulating stock cost model under different stock supply strategies and proposing a genetic algorithm. Compare total cost of various strategies and analysis the influence from different strategies, the result shows that:utilize of stock supply strategy could reduce supply chain cost.Third, in the environment of uncertainty demand and uncertain price of suppliers, considering uncertain production capacity, we study a multi-period supply chain integration plan under different stock supply strategies. Analysis the influence of stock cost and supply chain total cost from different strategies, in the condition that the key enterprise has uncertain production capacity. Formulating stochastic expected value model, using stochastic simulation based genetic algorithm to solve this problem. Through analyzing, we can conclude that:in the condition of uncertain production capacity, stock supply strategy can reduce the total cost of supply chain.Research shows that optimizing supply chain integration planning which formulating stochastic programming model and using genetic algorithm, and setting milt-period integration plan can effectively use resource and reducing cost.