Modularisation can dramatically shorten the lead time and reduce the cost of the product development. Most of the existing modularisation researches focus on the module partition, which is just the initial stage of an integrated modularisation implementation. With the increasing of
the module number, the product design engineers could not obtain the modules satisfying the emerging customer requirements (CRs) from the established module portfolio efficiently, especially in a complex product and system (CoPS) development. The CoPS module portfolio must be carefully established,
planned and managed, for which a CoPS module portfolio planning (MPP) framework is proposed to assist the design engineers to obtain the optimal module portfolio in CoPS development. First, the MPP problem for CoPS is modelled as a mixed-integer programming from two perspectives: the cost
utility of each parametric module and the information content for the function requirements (FRs) mapped from CRs. Second, the cost utility and the information content are calculated based on the part-worth model and the information axiom, respectively. Third, a heuristic genetic algorithm
(GA) is employed to solve the programming. Finally, a large tonnage crawler crane (LTCC) MPP problem is performed as an example to elaborate the effectiveness of the proposed framework.
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