Abstract
The effective and efficient assignment of orders to productive resources on manufacturing systems is relevant for industrial competitiveness. Since this allocation is influenced by internal and external dynamic factors, in order to be responsive, production systems must possess real-time data-drive integration. The attainment of this kind of integration entails relevant praxis and scientific challenges. In this context, this paper proposes an adaptive simulation-based optimization framework for productive resources scheduling which takes advantage of forthcoming data transparency derived from the application of digital factory concept. The proposed framework was applied in a test case based on a production line of a Brazilian automotive parts supplier. The outcomes substantiate the applicability of adaptive simulation-based optimization approaches for dealing with real-world scheduling problems. Furthermore, potential improvements on the management of dynamic production systems derived from the application of digital factory concept are also identified.
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Acknowledgments
This work is funded by Brazilian Federal Agency for Support and Evaluation of Graduate Education (CAPES) under reference number 99999.006033/2015-06, in the scope of BRAGECRIM program.
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Pimentel, R., Santos, P.P.P., Carreirão Danielli, A.M., Frazzon, E.M., Pires, M.C. (2018). Towards an Adaptive Simulation-Based Optimization Framework for the Production Scheduling of Digital Industries. In: Freitag, M., Kotzab, H., Pannek, J. (eds) Dynamics in Logistics. LDIC 2018. Lecture Notes in Logistics. Springer, Cham. https://doi.org/10.1007/978-3-319-74225-0_35
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DOI: https://doi.org/10.1007/978-3-319-74225-0_35
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