Price forecasting using an ACO-based support vector regression ensemble in cloud manufacturing

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.