Co-evolution of product family configuration and supplier selection: a game-theoretic bilevel optimisation approach

ABSTRACT

Joint decision making of configuration design and supplier selection has been well recognised for successful development of modular product families. However, the prevailing practice for integrating supplier selection into product family design is limited to only one family generation and neglects the changes in customer demands and technological progress over time, which calls for a co-evolution design across product generations from a systematic perspective, instead of separating optima.

In this paper, the co-evolution of configuration design and supplier selection for a product family is regarded as a distributed collaborative decision making, for which a Stackelberg game theoretic decision framework is proposed to model the interactions between product family planning in the current generation and product family re-planning in the next generation. Consistent with the Stackelberg game, a nonlinear bilevel optimisation model with a common deviation objective function, compromising an upper-level planning optimisation and a lower-level re-planning optimisation, is formulated. A corresponding nested bilevel genetic algorithm is developed to solve the optimisation model. A case study of mobile phone product family co-evolution with supplier selection is reported to illustrate the feasibility and potential of the proposed collaborative game theoretic approach.

KEYWORDS:

• Configuration design
• supplier selection
• product family co-evolution
• bilevel optimisation
• nested bilevel genetic algorithm

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was partially supported by the National Natural Science Foundation of China under Project No. 71371132. The effort of the first author in this paper was partially supported by the General Project of Humanities and Social Sciences Research in Tianjin Institution of Higher Education (2017SK073). The effort of the third author in this paper was partially supported by the National Science Foundation under Grant No. 1647335. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.