Research On Scale-based Product Family Positioning Optimization Method

With the increasing diversity of customer requirements and fierce of market competition, more and more enterprises turn to mass customization production mode in response to the tremendous changes of the external market environment. As one of the key technologies for the mass customization, platform-based product family design has received increasing attention by the academic and industrial circles.Under the pressure of the diversified requirements of customers, many enterprises blindly increase the variety of product family's product variants in order to get more orders and more profits. However, due to the rising complexity of the product family, the benefits haven't kept pace with this external variety. Product family positioning is used to deal with the problem- "how to offer right product variety to the right target market". It is one of the effective methods to weigh the external diversity and the internal commonality of the product family. In this paper, aiming at the characteristics of scale-based product family, an optimization method of parametric product family positioning is proposed. Based on the classification and modeling of the parameters, this paper presents two optimization indices for evaluating the internal and external quality of product family and establishes the product family positioning optimization model by using a new hybrid algorithm to solve the model. In the end, an application case is given as the authentication for the technology.Chapter 1 focuses on the research background and significance. Firstly, the significance of product family positioning is discussed. Secondly, the relevant research background is summarized and analyzed for the inadequacies by two aspects: product family design and product family positioning. The main content and the structure of this paper are given at last.Chapter 2 describes and models the product family positioning optimization problem. Based on the classification and modeling of the parameters, the profitability index of product family is presented in this paper, considering several factors including market demand coverage and distribution, customers'choice probability and product variant profit ratio. Moreover, the design parameters'commonality based on data aggregation is proposed. The two-objective optimization model is given at last.Chapter 3 is concerned with the hybrid algorithm for model solving, aiming at the specificity of the model. The hybrid algorithm is a combination of the multi-object genetic algorithm based on Pareto Elitist Strategy and the double gene-bit hill-climbing algorithm based on priori knowledge. At the end of this chapter, the comprehensive optimal selection method based on fuzzy set theory is also introduced.Chapter 4 introduces triplephase asynchronous motor as a case study to verify the product family positioning optimization model and the hybrid algorithm. Comparing with the result and process of the hybrid algorithm and general multi-objective genetic algorithm, the effectiveness and superiority of the hybrid algorithm are verified.Chapter 5 summarizes the achievements and innovations of this paper and gives a prospect of the future research in this field.