Mechanical engineering design optimization by differential evolution
New ideas in optimization
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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A fast and elitist multiobjective genetic algorithm: NSGA-II
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EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
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Product development in modern enterprises usually involves collaboration among designers, suppliers, contract manufacturers to achieve efficiency and rapid response to changing markets. Product-development cost, lead time, and reliability are very critical elements in addition to functional features. However, it becomes increasingly challenging to obtain an optimal decision with respect to these multiple criteria as the number of involved entities increases in modern product developments. There is a clear need for planning tools to support effective decision making in this domain. The availability of efficient and accurate multiobjective optimization (MOO) algorithms becomes critical in such a decision support tool. This paper poses the product development as a multiobjective assignment problem in the context of printed circuit board assembly (PCBA) industry. We describe a new class of MOO algorithm based on the principles of differential evolution (DE). The multiobjective DE (MODE) algorithm is shown to approach Pareto-optimal solutions in a wide class of problems with better performance than the nondominated sorting genetic algorithm II from the literature, providing a practical tool for product-development decision support. A decision support system based on the object-oriented design methodology is described in this paper with the MODE as the core search engine. Experimental study of this decision support system is conducted using two real-world PCBA designs. We demonstrate the effectiveness of this proposed MODE algorithm and some use cases of such decision support system on facilitating decision makers' tradeoff analysis.