Research On Supply Chain Optimization And Coordination Problems Based On Fuzzy Programming Models

Supply chain optimization and coordination mechanism is an important part of the supply chain management research.Building the mathematical programing models to optimize the issues of the supply chain optimization and coordination is one of the commonly used quantitateive research methods.In particular,linear programing,danamic programing and stochastic programing have been widely applied to supply chain manamgement.Fuzzy programming,as a new branch of mathematical programming,is a useful tool to solve fuzzy and random optimization problems,and has extensive applications in economic,management,financial,and investment areas.Supply chain optimization and coordination process also involves a lot of fuzzinesses,and is unable to be solved by typical mathematical programming techniques.Therefore,the research on supply chain optimization and coordination based on fuzzy mathematical programming is of actual application value and theoretical significance.From the perspective of fuzzy optimization algorithms for fuzzy programming,we analyze and classify the existing literature,and conclude that there are three main kinds of optimization algorithms for fuzzy grogramming:(a)Fuzzy optimization algorithms based on goal satisfaction numbership function;(b)Fuzzy optimization algorithms based on possibility measure;(c)Fuzzy optimization algorithms based on credibility measure.This paper attempts to study the usage of different fuzzy optimization methods for the problems of supply chain optimization and coordination,and construct the model to simulate the behavior of supply chain nembers.Different models are provided with the corresponding solution algorithms,and the problem-sloving steps are given.Concrete contents of the study can be concluded as follows:(1)Considering main influence factors like quality,price and service level,a multiobjective programming model for the vendor selection problem is constructed by using the modified S-type membership function to depict the degree of satisfaction of the selection goals.Then,we transform the proposed multi-objective programming model into a fuzzy compromise programming model,and the vendor selection is conducted by solving the fuzzy compromise programming model.To illustrate our methods,a comparison analysis with some other existing models is given.(2)Return policy in fuzzy environment between a manufacture and a retailer is studied,a fuzzy programming model based on Nash bargaining model with fuzzy coefficient is proposed,and the uncertainty demand is characterized as fuzzy numbers in the model.By means of defuzzying methods,we transform the fuzzy programming model into a typical optimization model,and finally,some practical examples are given to demonstrate the effectiveness of our model.(3)Pricing problem in fuzzy environment between a manufacture and two duopoly retailers is addressed,where the production cost and the market demand are assumed as fuzzy variables,the retailers might take competitive or collusive behavior with each other.According to the difference of the retailers' strategy,the Stackelberg models are formed respectively with fuzzy variables,and the models are resolved by fuzzy chance constrained programming,and the derived models are compared with some models in the existing literature.(4)Return policy with quantity discount in a supply chain with imperfect quality product is studied,in which the market demands are assumed as fuzzy numbers.Then a fuzzy programming model with fuzzy coefficient is developed,the model is resolved by mean of fuzzy cut set theory,and finally,a numerical example is given to illustrate the developed model.