An intelligent adaptive system for the optimal variable selections of R&D and quality supply chains

Abstract The cost of research & development (R&D) and quality management are always regarded as two major parts of total cost. The variable performance of R&D and quality design is an important index that will reflect the effectiveness of the cost reduction. This research has attempted to simultaneously vary all of the variables to achieve the global optimum for the optimal variable selections of R&D and quality design. Genetic algorithm (GA) can treat all of the variables for the global search. In this study, fuzzy refinement with orthogonal arrays was effective in improving the performance of the GA, and also showed the benefits of a good chromosome structure on the behavior of GA. It is also proposed the postponement design with temporal concept, to select the effective variables for the cost reduction of R&D and quality management design. The experimental results showed that tempo-postponement design will increase the flexibility and quick response for supply chain management. Hence, this approach can act as a useful guideline for researchers working on the optimization of the key variable selections for R&D and quality model design.

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