دانلود رایگان مقاله انگلیسی انتخاب تامین کننده و برنامه ریزی عملیاتی در زنجیره تامین زنجیره ای با عدم اطمینان عرضه - الزویر 2018

Abstract

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.

Conclusions

We have proposed a mathematical model to tackle the supplier selection and operation planning problem in biomass supply chains to help decision-makers facing uncertainty of biomass feedstock supply. The objective is to minimize the total system cost of a biomass supply chain. We have applied an enhanced and regularized L-shaped method to solve the two-stage stochastic programming model. This technique allows us to decompose a high dimensional stochastic model into subproblems of reasonable size. These sub-problems could be solved on a personal computer with limited memory. Moreover, our proposed method could find an optimal solution faster than the standard L-shaped decomposition method and commercial MILP solver Gurobi. Besides, we have analyzed the impacts of critical parameters on the optimal expected cost of the system and supplier 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. Our ultimate goal is to develop a decision-making support tool for biomass supply chain management.