Research On Multi-level-objective Optimization Of Supply Chain Based On Customer Satisfaction

Since the 21 st century,the supply chain has transformed into the demand-driven supply chain from the traditional production-driven supply chain with the development of the Internet,large data and cloud computing technology,especially the explosive development of mobile Internet.The initiative value of supply chain has been transferred from the core manufacturers to the hands of customers,so the influences of customer satisfaction on the profits of supply chain node enterprises are more and more obvious.More and more enterprises expand production,improve the production process,increase the frequency of sampling and other means to meet the customer demand,improve product quality,reduce the defective rate,and finally improve the customer satisfaction,but in this process the cost of business will be increased and corporate profits will be decreased inevitably.Therefore,it is very important for the long-term competitiveness of the supply chain to balance the relationship between customer satisfaction and the enterprise profits of each node in a cooperative way.It has become the hot and difficult area in the current supply chain optimization research to simultaneously optimize the customer satisfaction and the profit of the supply chain node enterprises by using the multi-level-objective programming theory and the related decision optimization method.Based on the analysis of the current supply chain decision-making optimization model and the optimization of customer satisfaction,the multi-level-objective programming theory is used in this paper to establish the three-level-objective decision-making optimization model of supply chain to coordinate customer demand and enterprise profits in a demand-driven supply chain in the case of changing demand.In the whole supply chain decision-making system,the coordination center,the manufacturer and the supplier respectively use the customer satisfaction,the manufacturer profit and the profit of the supplier as the decision-making goal,and each decision maker controls the different decision variable to carry on the decentralized decision-making optimization as the independent decision maker.In the multi-level-objective programming model,customer satisfaction is the first layer optimization target,the profit of the manufacturer is the second layer optimization target,and the profit of the supplier is the third layer optimization target.The customer satisfaction is given priority,and the profits of supply chain node enterprise are also taken into account.And the fuzzy membership method is used to transform the multi-level-objective programming problem into the single-level-objective programming problem.A three-step algorithm based on the interactive decision optimization method is designed.The first step,the equilibrium satisfaction of the decision makers is calculated by the maximum and minimum method,and each decision makers carry on optimization in an interactive way.The second step,the coordination mechanism is established to further optimize the customer satisfaction.The third step,the three targets are all improved by using the Nash equilibrium approach.Finally,the genetic algorithm is used to solve each step optimization model.In this paper,a supply chain decision optimization model based on multi-level-objective programming is constructed,and a three-step solution algorithm based on membership principle is designed,which enriches the current customer satisfaction optimization model and interactive decision optimization algorithm.The results show that the model and algorithm can keep the profits of enterprise while significantly improving customer satisfaction,and ultimately keep the customer satisfaction and corporate profits in a high level.In general,this paper has strong theoretical and practical significance.