Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Product platform design and customization: Status and promise
Artificial Intelligence for Engineering Design, Analysis and Manufacturing - SPECIAL ISSUE: Platform product development for mass customization
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Forming neural networks through efficient and adaptive coevolution
Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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Manufacturing enterprises are under competitive pressure to provide adequate product variety in order to meet diverse customer requirements while striving to reduce cost and time to market by employing product commonality and modularity. One successful approach to mass customisation (MC) is to design a family of product variants simultaneously to strike the optimum balance between commonality and differentiability. This paper formulates product family design as a multiobjective optimisation problem. A new method is proposed for assessing multi-level commonality at the product, module, component and even parameter levels. A multiobjective evolutionary algorithm (MEA) is proposed based on NSGA-II to solve this problem. This method uses a special scheme to represent and track the problem and its solutions. The effectiveness of the approach is first tested through a mathematical problem and then demonstrated with an industrial case of gantry crane family design. Computational experiments show favourable results and benefits of the proposed MEA-based product family design method.