Biomass supply chain network design: Integrating fixed and portable preprocessing depots for cost efficiency and sustainability

Bioenergy, as part of a broader renewable energy strategy, can significantly contribute to reducing greenhouse gas (GHG) emissions and combating climate change. However, high logistics costs remain a significant barrier to the growth of the bioenergy industry. This study introduces a novel Mixed Integer Linear Programming (MILP) model to optimize the biomass supply chain (BMSC) by integrating both fixed depots (FDs) and portable depots (PDs) for biomass preprocessing. The model optimizes the collection, transportation, and preprocessing of forest residue as biomass feedstock by determining the optimal number and location of both FDs and PDs, balancing costs associated with transportation, processing, and facility setup. Unlike traditional BMSCs, which rely exclusively on FDs, the inclusion of PDs provides the flexibility of relocating preprocessing units according to the availability of biomass. Scenario analysis and numerical experiments demonstrate that the integration of PDs can reduce total costs by up to 26.94 %, primarily through savings in transportation from biomass collection points to preprocessing facilities. This approach also enhances the efficiency of BMSC, enabling it to respond better to variable biomass availability and reduce environmental impacts. Further, the applicability of the optimization model is demonstrated through a real-life case study of a power plant in the state of Oregon, USA. This model provides valuable quantitative decision support for policymakers and energy stakeholders aiming at optimizing BMSC and contributing to global renewable energy targets.