Abstract
Incorporating financial hedging and sustainability in a supply chain is crucial for profit maximization or cost minimization. Uncertainties in supply chain develop into risks that affect the profit maximization or cost minimization expectations. In order to deliver end-products to destination markets in an efficient and effective manner, a supply chain management model that incorporates risk management measures is crucial. This paper develops a mathematical model that integrates hedging strategies in a biofuel supply chain with a corn and cellulosic raw material production setting. The paper is structured by first developing an optimization model considering maximization of the supply chain profit with risk without hedging for both corn and cellulosic biorefinery plants. Secondly, we incorporate sustainability concepts including environmental and social aspects. Finally, a heuristic method is developed for the hedging and a two-stage stochastic linear programming with Multi-cut Benders Decomposition Algorithm (MBD) is used to solve the problem. A case study in North Dakota is adopted for this study. The results for hedging and non-hedging are compared and sensitivity analyses conducted.
Highlights
Risk management and sustainability are crucial aspects of the supply chain that have gained enormous attention by researchers and practitioners
A heuristic method is developed for the hedging and a two-stage stochastic linear programming with Multi-cut Benders Decomposition Algorithm (MBD) is used to solve the problem
This paper develops a model that uses hedging strategies in a Renewable Energy Supply Chain (RESC) renewable energy supply chain (RESC) setting
Summary
Risk management and sustainability are crucial aspects of the supply chain that have gained enormous attention by researchers and practitioners. We first define the term Renewable Energy Supply Chain (RESC) which is a combination of corn-based and cellulosic-based biofuel supply chain. Especially hedging in biofuel supply chain is limited This makes it important to develop optimization models that effectively integrate hedging decisions in the supply chain decision process. Research novelties such as: 1) developing a heuristic method for the hedging; 2) modeling the corn feedstock and ethanol price uncertainties as Mean Reversion (MR); and 3) developing a hybrid biorefinery supply chain which consists of corn and cellulosic feedstock biorefinery plants.
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