Data-driven robust optimization to design an integrated sustainable forest biomass-to-electricity network under disjunctive uncertainties

The consequences of using costly, nonrenewable, and nonsustainable fossil fuels have forced countries to focus on renewable energy resources. In addition, the necessity of smooth material and information flow in the supply chain has motivated scholars to invent different mathematical approaches to designing an integrated supply chain. Such an integrated biomass-to-electricity supply chain can guarantee smooth material and information flow in the supply chain. To this end, a data-driven robust optimization approach under disjunctive uncertainties is developed for designing and optimizing an integrated sustainable biomass-to-electricity supply chain network. Instead of assuming uncertain parameters data to be less scattered or almost uniform, an uncertainty set based on data with disjunctive structures is proposed to identify the uncertainty space more accurately and flexibly. Specifically, the data on uncertain parameters are clustered using the K-means technique. Then, the primary uncertainty sets are formed for each cluster. The proposed uncertain parameter space is constructed by incorporating these primary uncertainty sets, which form using support vector clustering. We studied a case in Iran to probe the performance of the proposed approach. The computational results indicate that the proposed framework provides maximum protection against disjunctive uncertainty with minimum robustness price.