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yThe growing environmental consciousness resulted from global climate changes has aroused petrochemical industries to search for the renewable alternatives for fossil fuels. Recently, biomass has been received increasing attention due to its economic and environmental benefits. Olefin, as one of the key raw materials in petrochemical industries, is able to be produced from biomass feedstocks. This study presents a robust three-stage stochastic programming model to characterize and optimize an olefin supply chain/production network aiming to provide a reliable and economic logistics network to support olefin production. This model encompasses probabilistic scenarios and uncertainty sets to capture the seasonality of biomass feedstocks and the uncertainty of carbon tax rate, respectively. The Municipal Solid Waste (MSW) is also involved in this model to complement the traditional biomass supplies to ensure the reliable feedstock for olefin production. To find the optimal solution of this model, a hybrid robust/stochastic approach is developed by integrating the affinely adjustable robust model with the sample average approximation (SAA) method. The state of Mississippi is used as a real case study to test and validate the proposed model and optimization approach. The results show that increasing feedstocks conversion rate by 20% and MSW recycling rate by 100% will increase olefin production by 17.26% and 14.3%, respectively, and increasing the carbon tax rate uncertainty from 0 to 30 will decrease the total network emissions by 2.8%. The proposed optimization approach will generate more robust and reliable results. These results indicate that the proposed model and optimization approach would benefit both economic and environmental perspectives in biomass based olefin production. (C) 2019 Elsevier Ltd. All rights reserved.