Research Of Product Family Design And Planning Considering Market Factors

With the current changing market, customer demands for products become increasingly complex and diverse. As a common technology positioning in a series of interconnected market, product family is paid more and more attention by enterprises in market competition. As a result, product family design and planning issues also come into being.Product line design is a key decision problem that a product development team has to deal with in the early stages of product development. Previous studies of product line design have focused on single objective optimization. Therefore, the optimal or near optimal solution can be obtained by applying the simplex method or heuristic algorithms. However, several optimization objectives may be simultaneously pursued, and the solutions that can address the objectives are required in many practical scenarios.The basic idea of this research is to use multi-objective genetic algorithm for solving the design and planning of product family under the influence of market factors in the multi-objective optimization model. Three optimization objectives are considered:to maximize market share of the company’s products, to minimize the total development cost of product family, and to minimize product family development cycle. In the solving process, a one-step multi-objective optimization approach is proposed for product line design. Curve fitting approach is introduced to process the attributes with continuous attribute values in some practical model. The proposed multi-objective genetic algorithm can solve the multi-objective optimization model and obtain a set of non-dominant solutions.The main contribution of this paper includes:(1)Build the multi-objective optimization model for product family design. The model introduces a component model to represent the utility level of product attributes, attribute values and the relationship between the product effective,and using probabilistic customer choice rule to simulate the customer choice behavior.(2)Design of a multi-objective genetic algorithm. Solve the optimization problem of the proposed model and implement a set of non-dominant solution. With these non-dominant solutions, decision makers of a company can choose the right solution interactively.(3) Design of digital cameras are used as an example to evalute the proposed multi-objective optimization model and multi-objective genetic algorithm.