Robust design and planning of microalgae biomass-to-biodiesel supply chain: A case study in Iran

Abstract

Microalgae have emerged as one of the most promising sources for biodiesel production because they yield a substantial amount of oil. In order to accelerate the commercialization of microalgal biodiesel, this paper proposes a two-stage model for the design and planning of a microalgae-based biodiesel supply chain. The macro-stage performs a spatial filtering using GIS and AHP to identify the most suitable candidate locations to establish biodiesel production facilities. These potential locations are later applied in the supply chain design model of the micro-stage. Consequently, the macro-stage obviates the need to consider a large set of candidate locations which is the main reason for the computational complexity of the supply chain optimization problems. In the micro-stage, a robust mixed-integer linear programming (RMILP) optimization model, which provides a trade-off between system cost and reliability, is elaborated to determine the strategic and tactical supply chain decisions that remain optimal for almost all possible realizations of the uncertain parameters. The applicability of the proposed framework is demonstrated through a case study considering different uncertainty settings. The results show that the proposed model outperforms the traditional supply chain design models in terms of solution robustness and computational time.