Product line selection and pricing under uncertainty

Product line selection and pricing are important decisions in the early stages of product development. In past research, optimization models on product line selection and pricing have used only the crisp model parameters, meaning that they can be estimated accurately. However, some of the model parameters are prone to be inaccurate due to the inherent uncertainty of human knowledge and human expression. For example, market demand is estimated by human experts, market survey data are collected from invited consumers, and development costs are estimated by product development team. In this research, these parameters are considered as triangular fuzzy numbers and establish a one-step fuzzy optimization model for product line selection and pricing. An interactive decision-making approach is proposed to solve the fuzzy model and a linear-programming-embedded genetic algorithm is specially devised for the corresponding crisp model with a given acceptable degree of membership functions given by decision maker. An example of printing calculator product is used to illustrate the proposed approach. Experiments and sensitivity analysis based on the illustrative example are also performed to analyze the relationship among the parameters and to explore the characteristics of the optimization model.