Multiobjective evolutionary algorithms (MOEAs) have been widely used in real-world
applications. However, most MOEAs based on Pareto-dominance handle many-objective …

In this paper we focus on scale-based product families derived from scalable product
platforms that can be exploited from both a functional and a manufacturing standpoint to …

Decomposition-based evolutionary algorithms have been quite successful in solving
optimization problems involving two and three objectives. Recently, there have been some …

The use of statistical techniques to build approximations of expensive computer analysis
codes pervades much of today's engineering design. These statistical approximations, or …

In this paper, we compare and contrast the use of second-order response surface models
and kriging models for approximating non-random, deterministic computer analyses. After …

Many-objective optimization problems (MaOPs) contain four or more conflicting objectives to
be optimized. A number of efficient decomposition-based evolutionary algorithms have been …

The objective of robust design is to optimise the mean and minimize the variability that
results from uncertainty represented by noise factors. The various objective functions and …

After the industrial revolution, the literature has mentioned different “principles” to allow a
better management of the production and product life cycle activities. For example the …

The rationale of developing product families with respect to satisfying diverse customer
needs with reasonable costs, ie, mass customization, has been well recognized in both …

Multi-objective optimization problems in many real-world applications are characterized by
computationally or economically expensive objectives, which cannot provide sufficient …