Designing Resilient Biomass Energy Supply Networks under Environmental Disruptions and Market Uncertainty: An Integrated Data-Driven Clustering, MCDM, and Bi-Level Optimization Framework

Abstract

Given the growing energy demand, limitations of fossil resources, and the urgent need to mitigate environmental impacts, developing renewable energy supply networks, especially those based on biomass, has gained significant importance. Accordingly, this study proposes a hybrid multi-phase decision-making framework for the optimal design of a resilient biomass energy supply network under environmental and market uncertainties. In the first phase, spatial data from 120 agricultural sites across eight provinces of Iran were clustered into homogeneous groups using four data-driven algorithms, namely Constrained K-Means (CKM), the Gaussian Mixture Model (GMM), Spectral Clustering, and Affinity Propagation (AP). Subsequently, Bayesian Best-Worst and Best-Worst PROMETHEE methods were employed to weight and rank decision criteria, facilitating the identification of optimal hub locations. The resulting clusters and hub scores were then incorporated as input parameters for the network design problem: in the first phase, hub location and cluster-to-hub allocation were determined; in the second phase, a bi-level, multi-period mathematical model was developed to optimize network flows and intra-cluster routing under uncertainty. Numerical results reveal that employing Spectral Clustering led to a 28% reduction in total supply chain costs compared to the uncluttered baseline, with other algorithms achieving reductions between 10% and 19%. This research, grounded in real-world data collected from eight provinces, demonstrates the effectiveness of data-driven and multi-criteria approaches in enhancing the sustainability, cost-efficiency, and resilience of biomass energy supply networks.