A Multi-objective Collaborative Optimization Algorithm Based on Cycle Subsystem Model with Features for Product Family Design

The capability of product family design meets new requirements of customer-driven market nowadays. In order to improve the market competence, enterprises need design a certain product family with better quality, lower cost and less time spending as its best, while these three optimal goals are contradictory in most instances. In this paper, a novel multi-objective collaborative optimization algorithm, Non-dominated Sorting Genetic Algorithm with Hill Climbing algorithm (NSGA-HC), is proposed for product family design, which can trade off local bias of the single-factor algorithm. Secondly, in terms of a better product family designing scheme, a Cycle Subsystem Model with Features is structured, which not only considers product features as an essential factor influencing product performance but also regards each subsystem as individual for product family design. In addition, an industrial field example is demonstrated as a multi-objective optimal problem to validate the proposed model and algorithm. Finally, an experimental comparison shows that this novel algorithm has higher efficiency and avoids local extremum.