{"id":2912,"date":"2022-07-13T09:30:00","date_gmt":"2022-07-13T07:30:00","guid":{"rendered":"https:\/\/cirpicme.org\/?page_id=2912"},"modified":"2022-07-26T13:05:07","modified_gmt":"2022-07-26T11:05:07","slug":"a-machine-learning-approach-for-revenue-management-in-cloud-manufacturing","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/cyber-physical-systems-digital-manufacturing-2\/a-machine-learning-approach-for-revenue-management-in-cloud-manufacturing\/","title":{"rendered":"A Machine Learning Approach for Revenue Management in Cloud Manufacturing"},"content":{"rendered":"\n

by Vincent Adomat, Jonas Ehrhardt, Christian Kober, Maryam Ahanpanjeh, Jens P. Wulfsberg (Germany)<\/em><\/p>\n\n\n\n

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Abstract<\/strong><\/p>\n\n\n\n

Cloud Manufacturing matches supplier capacities and customer demands via an algorithmic agent. The agent aims to maximize its own and the platform participant\u00d5s revenue, hence the platform\u00d5s 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.<\/p>\n\n\n\n

Keywords<\/strong>: Cloud Manufacturing; Machine Learning; Recurrent Neural Network; Prediction; Forecasting; Optimization; Revenue Management; Industry 4.0<\/p>\n\n\n\n

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Video presentation<\/strong><\/p>\n\n\n\n