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Potential of data-driven simulation-based optimization for adaptive scheduling and control of dynamic manufacturing systems.

, , , , and . WSC, page 2820-2831. IEEE, (2016)

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Potential of data-driven simulation-based optimization for adaptive scheduling and control of dynamic manufacturing systems., , , , and . WSC, page 2820-2831. IEEE, (2016)Towards adaptive simulation-based optimization to select individual dispatching rules for production control., , , , and . WSC, page 3852-3863. IEEE, (2017)Including operator's skill and environment conditions in IMS., , , , , , , , and . INDIN, page 295-300. IEEE, (2014)Collaborative Distributed Operational Planning for Spare Parts Supply Chains., , , , , and . LDIC, page 95-101. Springer, (2018)Hybrid flow shop rescheduling for contract manufacturing services., , and . Int. J. Prod. Res., 60 (3): 1069-1085 (2022)Cloud-Based Additive Manufacturing as a Strategy for Product Variety: A Simulation Study., , and . SOHOMA, volume 803 of Studies in Computational Intelligence, page 53-66. Springer, (2018)Proposal of a Reconfigurability Index Using Analytic Network Process., , , , , and . IEEM, page 1310-1313. IEEE, (2019)Production paradigms for additive manufacturing systems: A simulation-based analysis., , , and . ICE/ITMC, page 973-981. IEEE, (2017)Manufacturing networks in the era of digital production and operations: A socio-cyber-physical perspective., , , and . Annu. Rev. Control., (2020)Sustainability and Effectiveness in Global Supply Chains: Toward an Approach Based on a Long-term Learning Process., and . LDIC, page 251-258. Springer, (2007)