In today’s competitive global business environment, platform strategy presents an opportunity for manufacturing companies to juggle increased customer demand for customized products and the inherited complexity and increased development cost that comes with it. The goal of this paper is to support module identification as an essential part of a module-based platform strategy approach. Based on various existing methods, this paper abstracted three principles, which include an internal clustering principle, an external independence principle, and an overall stability principle. The three principles should be holistically considered, and be simultaneously satisfied during the module identification. Both conceptual and mathematical modeling of the proposed multi-principle module identification method are elaborated. Then an improved strength Pareto evolutionary algorithm (ISPEA2) is used to address the multi-principle module identification problem and find the Pareto-optimal set. A fuzzy compromise selection method base on fuzzy set theory is also used to select the best compromise Pareto solution. An industrial case study in a turbo expander manufacturing company is provided to illustrate practical applications of the research. Finally, the result obtained by the proposed approach is compared with other established optimization approaches.
