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论文类型：期刊论文

发表时间：2018-11-01

发表刊物：COMPUTERS & INDUSTRIAL ENGINEERING

收录刊物：SCIE、SSCI

卷号：125

页面范围：171-177

ISSN号：0360-8352

关键字：Price forecasting; Parametric pricing; Ant Colony Optimization; Ensemble; Cloud manufacturing

摘要：In a cloud manufacturing environment, deviation of a service price from its value and cost makes on-demand price forecasting a challenging task for the service providers. The main objective of this paper is to present taxonomy of Value Measures and Metrics (VMMs) for pricing decisions over the product life cycle, e.g. the design, manufacturing, and service stages. Furthermore, a parametric pricing approach is proposed to formulate pricing variables, which represent pricing factors and are calculated in terms of VMMs, as well as a regression relation between the pricing variables and price. An Ant Colony Optimization Algorithm (ACO)-based Support Vector Regression (SVR) ensemble is developed to forecast a price. We demonstrate the effectiveness of the proposed methodology with the real-world data of an organization in China. The experimental results show that the proposed method achieves significant generalization performance with the best mean squared error (MSE) and reliable results in randomness of ensemble learning. Thus, the proposed pricing method provides a way to make viable prices for service providers.