Abstract
Recently, the biomass has gained considerable attention as a feedstock for energy production because of its attractive characteristics, including its availability as a renewable resource. However, the biomass can be subjected to several uncertain factors such as the availability, market cost and composition; thus, it is worth noting that the uncertainty in the raw material can affect drastically the final supply chain configuration of the product. Therefore, this work presents a new approach for the optimal planning under uncertainty for a biomass conversion system involving simultaneously economic and environmental issues. In this context, the EcoIndicator99 method was used to assess the overall environmental impact in the entire supply chain. Additionally, the economic aspect takes into account all the costs associated to the different activities as well as the costs for raw materials and the sale of products. The proposed method considers the uncertainty involved in the supply chain through the raw material price by the stochastic generation of scenarios using the Latin Hypercube method followed by the implementation of the Monte- Carlo method for determining the optimal structure for each sample. Furthermore, with the proposed approach is possible to select the more robust structure for the supply chain based on statistical data. On the other hand, the proposed approach incorporates an analysis based on the standardized regression of the uncertain coefficients within the supply chain to determine the magnitude in which the uncertain data affect the value of the considered objectives. The proposed approach was applied to a case study for a distributed biorefinery system in Mexico, considering 6 suppliers, 6 processing facilities as well as 5 distribution centres. Besides, 9 raw materials were contemplated to obtain 5 different products through 2 processing routes.
