Multi objective -Multi-level Scheduling in Cloud Manufacturing: A Hybrid Approach Integrating Mathematical Modeling and Machine Learning

Document Type : Original Article

Authors

1 Department of Industrial Engineering, Qa.C., Islamic Azad University, Qazvin, Iran

2 Department of Industrial Engineering, Golpayegan Isfahan University of Technology, Golpayegan, Iran

10.22091/jemsc.2026.14343.1319

Abstract

This research investigates multi-objective, multi-level scheduling within a cloud manufacturing environment, employing metaheuristic algorithms. Initially, a comprehensive literature review was conducted, followed by the development of a multi-objective mathematical model integrated with a machine learning (ML) model. The model’s validity was first assessed by solving it for small-scale instances. Given that the exact method was only feasible up to the tenth instance, metaheuristic algorithms were utilized for solving the model in larger dimensions. The results demonstrated the model’s solvability in large-scale scenarios using the NSGAII algorithm. Subsequently, the model was solved considering risk input values, revealing that among the temporal parameters, transportation time and setup time exhibit the most significant impact on overall time. Among the cost-related parameters, preparation cost has the greatest effect on time, with transportation cost being the next most significant. Crucially, activity time emerges as the most impactful cost parameter, with transportation cost following as the second most influential.

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References

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