CLSC's Pricing Optimization On The Basis Of Simulated Annealing Algorithm

With the increasing awareness of global resource utilization and environmental protection, remanufacturing and reverse logistics, especially the research of closed-loop supply chain has become a widespread focus of the business community and academic.On the one hand, closed loop supply chain management can reduce energy consumption, reduce environmental pollution, and realize the recycling of resources.On the other hand,closed loop supply chain management can optimize the manager's decision-making,enhances the enterprise the competitive power,and achieve the goal of comprehensive benefit of economy and environment.Firstly,the paper reviews the related theory of closed loop supply chain,and introduces the development of closed loop supply chain in detail,and describes two pricing models of remanufactured products and new products pricing differences and no differences,and introduces uncertain management of closed loop supply chain,and describes the application of optimization algorithms in various fields.Secondly,the paper analyses the complex contact of recycled product's price,quantity and quality,and use the fuzzy toolbox of MATLAB to establish the corresponding Mamdani fuzzy inference system.To simulate and analysis the result,we prove that the Mamdani fuzzy rule is reasonable.Then,I set up a series of parameters and variables and the pricing model.Thirdly,the paper describes the operation principle of genetic algorithms,simulated annealing algorithm and genetic simulated annealing algorithm and use three kinds of optimization algorithms to simulate the two kinds of pricing models.By comparing the respective optimal fitness and the number of iterations,we can find that genetic simulated annealing algorithm can do the best.So,we can use this to help manager make better decisions of pricing.Finally,by comparing two economic models at the same market and analysing the influence of parameters change,we can give some experiences to manager to make better decisions of production planning.