Study Of Robust Model Of Open Innovation On Supply Chain Value

With the arrival of knowledge economy times,the traditional "closed" innovation model has become difficult to adapt to the current development costs,shortened product life cycle and increasingly global competition.Open innovation model which break down the enterprise boundaries and obtain external resources arises at the historic moment.So the open innovation's influence on the enterprise value and even the whole supply chain value is particularly important.Due to the effect of various factors,the real effect of innovation is difficult to determine before it's real implement.So this paper fully consider the uncertainty caused by innovation effect,studied the problems of supply chain value.Firstly,according to the operation condition of the supply chain,nonlinear programming model was constructed which maximize the manufacturers benefit as the objective function,and meet the retailers' and open innovation subjects' expected benefit as constraint condition.At the same time consider the uncertainty of open innovation effect,we introduce of uncertain interval ui?[ui0-ui,ui0+ui],J established nonlinear programming model with uncertainty--uncertain programming model.In view of the uncertainty of uncertain programming,simulate the random behavior of swarm intelligence algorithm,the discrete PSO algorithm is designed.Secondly,considering influence of uncertainty to the objective function and the decision variables,we introduced parameter r in the objective function and using robust optimization approach to analyze the original model,convert the nonlinear programming model with uncertainty into a robust model,the robust model is a non-convex nonlinear programming--DC programming.In this study,integer variables contained in the DC programming,so we introduced penalty function combined with the branch and bound algorithm and DCA algorithm,designed the B&B-DCA algorithm which has global convergence.Finally we design Numerical example for the algorithm,it Shows that the discrete PSO algorithm for single experiments has good convergence,but many times test,it still has very strong randomness,lack of stability;On the contrary,robust optimization method has good stability and validity and B&B-DCA algorithm has better global convergence.And examples further illustrate,along with the increase of uncertainty data in supply chain,manufacturers could get less information,the supply chain profit decreased at the same time.So manufacturers could select the right value,choice reasonable ?,and make reasonable production planning,according to their own preferences on profits and uncertainty degree.This study provides a theoretical support for the effect of open innovation research,and has very important significance for the research under uncertain environment of supply chain value.