Affiliations: [a] School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| [b] Department of Industrial Engineering, School of Mines, China University of Mining and Technology, Xuzhou, Jiangsu, China
Correspondence:
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Corresponding authors. Yupeng Li, Department of Industrial Engineering, School of Mines, China University of
Mining and Technology, Xuzhou, Jiangsu, China. E-mail: [email protected] and Xuening Chu, School of
Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China. [email protected].
Abstract: The evolution of the product family is the essential driving force for the development of a complex product. Only customer satisfaction is emphasized in the traditional module configuration methods, which is not beneficial for product family evolution that is due to non-customer factors such as the emergence of new technology. In this study, the intuitionistic fuzzy number is employed to quantify the degree of correlation between each module and configuration targets, namely customer satisfaction and the degree of evolution of the product family, respectively. The bi-objective integer programming model is constructed by maximizing the degree of customer satisfaction and product family evolution. An improved Pareto ant colony optimization (P-ACO) is designed to solve this model and subsequently the Pareto frontier is obtained. The radar chart is adopted to represent the performance of each configuration scheme in the Pareto frontier. The feasibility and effectiveness of the proposed method are expounded by a case study and result comparison, showing that this method can provide a more competitive product configuration scheme to customers in the future market.
