A two-stage stochastic programming model for biofuel supply chain network design with biomass quality implications

Abstract

Biofuel, an efficient alternative to fossil fuels, has gained considerable attention as a potential source to satisfy energy demands. Biomass collection and distribution typically incur a significant portion of the biofuel production cost. Thus, it is imperative to design a biofuel supply chain network that not only aims to minimize the delivery cost, but also incorporates biomass quality properties that make this raw material so unique yet challenging. This article proposes a novel two-stage stochastic programming model that captures different time- and weather-dependent biomass quality parameters (e.g., the moisture content, ash content, and dry matter loss) and their impact on the overall supply chain design. To efficiently solve this optimization model, we propose a parallelized hybrid decomposition algorithm that combines the sample average approximation with an enhanced progressive hedging algorithm. The proposed mathematical model and solutions are validated with a real-life case study. The numerical experiments reveal that the biomass quality variability impacts the supply chain design by requiring additional depots, and therefore, it increases the capital investment. The storage of unprocessed biomass at depots and biorefineries decreased by 88.5% and 97.9%, respectively, and the densified biomass inventory at biorefineries increased 17-fold when baseline quality considerations were taken into account.