A Scheduling Decision-Making Framework Using Machine Learning Algorithm for Energy Efficient Integrated Factory

Hariketan Patel

doi:10.1007/978-3-032-07675-5_52

A Scheduling Decision-Making Framework Using Machine Learning Algorithm for Energy Efficient Integrated Factory

2026-01-01

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DOI：10.1007/978-3-032-07675-5_52

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Sustainability enhancement is a key factor in industrial decisionmaking. Maximizing energy-efficient factory use to fulfill customer order deadlines is a substantial problem for manufacturers. This paper focuses on the study of the integrated factory with four different route configurations. Two dispatching rules (First come first serve and end due date) are considered for investigations. First, six different machine learning algorithms were applied for performance characterization, including Mean Absolute Lateness (MAL), Root Mean Squared Lateness (RMSL), and Fill Rate (FR) to predict effective utilization of the framework. Then, the energy utilization data of different configurations will be combined with a predictive framework to focus the decision strategy for sustainable production. The robustness of the proposed strategy is examined by metrics of tests. Our results illustrate the application of machine learning approaches in decision-making to execute efficient scheduling decisions and sustainable manufacturing.

Keywords:

Integrated factory

Sustainability

Energy efficient

machine learning

A Scheduling Decision-Making Framework Using Machine Learning Algorithm for Energy Efficient Integrated Factory

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