A fuzzy dynamic multi-objective multi-item model by considering customer satisfaction in a supply chain

Customer satisfaction is an important issue in competitive strategic management of companies. supply chain logistical and cross-functional drivers have an important role to manage customer satisfaction. customer satisfaction depends on quality, cost and delivery. in this paper a fuzzy mixed integer nonlinear programming model is proposed for a multi-item multi-period problem in multi-level supply chain. minimizing costs, manufacturing and transportation time, transportation risks, maximizing quality by minimizing the number of defective products and maximizing customers’ service levels are considered to be objective functions of the model. furthermore, it is assumed that the demand rates are fuzzy values. an exact -constraint approach is used to solve the problem. the problem is computationally intractable. therefore, the non-dominant sorting genetic algorithm (nsga-ii) is developed to solve it. the taguchi method is utilized to tune the nsga-ii parameters. finally, some numerical examples are generated and solved to evaluate the performance of the proposed model and solving methods.