Elyasi, MiladÖzener, Başak AltanEkici, AliÖzener, Okan ÖrsanYanıkoğlu, İhsan2023-08-082023-08-0820222405-8963http://hdl.handle.net/10679/8585https://doi.org/10.1016/j.ifacol.2022.10.195The ongoing pandemic, namely COVID-19, has rendered widespread economic disorder. The deficiencies have delayed production at manufacturers in several industries on the supply side. The effects of disruption were more notable for industries with longer supply chains, especially reaching East Asia. Regarding the demand, sectors can be divided into three categories: i) the ones, like e-commerce companies, that experienced augmented demand; ii) the ones with a plunged demand, like what hotels and restaurants experience; iii) the companies experiencing a roller-coaster-ride business. After mitigation efforts, the economy started recovering, resulting in increased demand. However, regardless of their struggles, the companies have not fully returned to their pre-pandemic levels. One of the strategies to gain resilience in its supply chain and manage the disruptions is to employ flexible/hybrid manufacturing systems. This paper considers a flexible/hybrid manufacturing production setting with typically dedicated machinery to satisfy regular demand and a flexible manufacturing system (FMS) to handle surge demand. We model the uncertainty in demand using a scenario-based approach and allow the business to make here-and-now and wait-and-see decisions exploiting the cost-effectiveness of the standard production and responsiveness of the FMS. We propose a column generation-based algorithm as the solution approach. Our computational analysis shows that this hybrid production setting provides highly robust response to the uncertainty in demand, even with high fluctuations.engAttribution-NonCommercial-NoDerivatives 4.0 InternationalopenAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/conferenceObject55103040304500088168170045810.1016/j.ifacol.2022.10.195Design and reconfiguration of manufacturing systemsFacility planning and materials handlingInventory control, Production planning and schedulingStochastic methods2-s2.0-85144549884