What is it about?

Bioenergy is considered a potential solution to reduce carbon footprint and fight against global warming. However, uncertainty in the harvest of biomass could lead to the instability of feedstock supply that has a significant impact on the sustainability of biomass supply chain. In this paper, we present a two-stage stochastic programming model dealing with supplier selection to stabilize feedstock supply of a biomass supply chain in uncertain environments. The model involves the first stage decisions for the supplier selection and the second-stage decisions for planning transportation, inventory and production operations. To reduce the computational burden for large instances, we propose an enhanced and regularized L-shaped decomposition algorithm to solve the model. The applicability of this model and the performance of the solution method are evaluated by numerical studies. Sensitivity analysis shows that the values of some parameters related to suppliers have significant impacts on the optimal expected cost and supplier selection.

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Why is it important?

Firstly, we propose a two-stage stochastic programming model for biomass supply chain management. In the model, the first stage decision is to select suppliers who provide long/mid term feedstock supply. The second-stage decisions determine the amount of biomass to purchase, to transport, to pre-process and the volume of bioethanol to produce in each period. Secondly, we develop an enhanced and regularized L-shaped decomposition method to solve the stochastic programming model with a given number of scenarios. The Monte Carlo sampling approach is used to find the required number of scenarios for ensuring that the exact value of an output of the model is located within a confidence interval with a given probability. Thirdly, we conduct a numerical study to evaluate the performance of the proposed algorithm. The numerical results show that the algorithm can find an optimal solution in a reasonable computation time while a commercial solver cannot for large instances. Sensitivity analysis is performed to investigate the effect of some critical parameters on the optimal expected cost and suppliers selection


Several directions for future research may be pursued as considering other uncertainties (price, quality, external demand, conversion technology). Another possibility is to integrate a more detailed transportation planning with the number of truck trips as a decision variable in each period. For this extension, the second stage problem may become a MILP model which imposes a high computational challenge. More effective algorithms may be developed to solve the complex and high dimensional model.


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This page is a summary of: Supplier selection and operation planning in biomass supply chains with supply uncertainty, Computers & Chemical Engineering, October 2018, Elsevier, DOI: 10.1016/j.compchemeng.2018.07.012.
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