Customer-driven product configuration optimization for assemble-to-order manufacturing enterprises

Configuration design has been recognized as an effective means to implement mass customization. Traditional configuration technologies mostly focus on practices from an engineering perspective on the basis of constraint and experience, yet leave the customer's viewpoints intact. In this paper, a new optimization approach for customer-driven product configuration is proposed. Configuration space is firstly established as the foundation for targeting diversity of customer needs and utility function is then employed to model and measure customer preference. Subsequently, a mathematic model that maximizes the ratio between overall utility and cost from both perspectives of customers and manufacturers is formulated, and a genetic algorithm is adopted to solve the combinatory optimization problem wherein the nested encoding scheme and multiple constraints handling are incorporated to improve the performance of configuration solving. A case study of a notebook computer is reported to demonstrate that the proposed approach provides an effective means for ATO manufacturing enterprises to deliver the optimal configuration solution.