A Machine Learning Approach for Revenue Management in Cloud Manufacturing.

Cloud Manufacturing matches supplier capacities and customer demands via an algorithmic agent. The agent aims to maximize its own and the platform participant's revenue, hence the platform's gross revenue. Volatilities in supply and demand, as well as individual pricing models and willingness to pay, exacerbate the problem for deterministic solvers. In Revenue Management, comparable problems are approached with linear programming and regression models. Within this paper, we introduce a new Machine Learning (ML) based approach: Machine Learning for Cloud Manufacturing Cost Prediction (ML-ConCord). ML-ConCord aims to improve prediction quality for Revenue Management in Cloud Manufacturing by combining a recurrent neural network (RNN) with a linear programming pricing algorithm. We evaluate our approach on a self-generated dataset and compare its performance with standard regression-based approaches. Models and datasets are published on GitHub. [ABSTRACT FROM AUTHOR]