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Chemical supply chains are a crucial component in the ongoing supply of large population centres. Unfortunately, episodes of extreme weather have, in recent years, revealed vulnerabilities in global supply networks to high-impact events. With a possible increase in both frequency and intensity of these events due to climate change, supply chains are at risk of disruption now more than ever, with potential dire economic, societal, and environmental consequences. Acknowledging that the direct application of stochastic programming in this context can quickly lead to very large CPU times, we propose an algorithm that combines the sample average approximation method with a selection heuristic for extreme event scenarios. Our method allows to analyse the tradeoff between economic performance and disruption risk, identifying supply chain configurations which are more resilient against extreme events. We demonstrate the effectiveness of this methodology in multiple case studies, showing how it identifies near optimal solutions in short CPU times. (C) 2019 Elsevier Ltd. All rights reserved.