Biomass procurement and supplier diversification for energy generation: Optimization models and insights

Abstract

This study addresses the challenge of designing cost-efficient and resilient biomass procurement strategies by integrating supplier diversification and contractual flexibility into a mathematical optimization framework. We formulate a mixed-integer linear programming (MILP) model that captures key characteristics of the procurement context, including biomass heterogeneity, moisture variability, storage capacity constraints, logistics, and monthly energy demand. The model reflects the operational and contractual conditions faced in the strategic design of biomass supply chains. A multi-objective extension is introduced to analyze trade-offs between minimizing procurement costs and maximizing the number of contracted biomass profiles, which serves as a proxy for supply diversification.

The model is tested through a real-world case involving a centralized boiler facility within a manufacturing firm in the Biobío region of Chile, where thermal energy supports the industrial process. Computational experiments show that moderate diversification incurs minimal additional costs, whereas aggressive diversification results in sharply increasing marginal costs. Moreover, greater contractual flexibility consistently lowers procurement expenses.

To assess trade-offs among cost, supplier diversification, and contractual flexibility, the $\epsilon$-constraint method is used to generate Pareto frontiers under varying flexibility scenarios. This approach provides decision-makers with a structured framework to explore feasible procurement strategies across the cost–diversification–flexibility spectrum.

Our results underscore the importance of integrated modeling approaches in striking a balance between economic efficiency and supply chain resilience. The proposed framework provides practical guidance for procurement managers and supports data-driven contract design in biomass-based energy systems. All model files, data sets, and results are publicly available to ensure transparency and reproducibility.

Additionally, we empirically validate the resilience of the resulting procurement plans through post-hoc disruption case studies, quantifying procurement fulfillment under pure and mixed substitution strategies.