| Summary: | 碩士 === 國立成功大學 === 製造資訊與系統研究所 === 104 === During the past decade, the demand for biofuel has rapidly increased because of fossil fuel scarcity, which has resulted in the accelerating commercialization of the global biofuel industry. The development of international biofuel markets is expected to produce a global bioenergy potential of approximately 500 EJ per year by 2050. This study proposed a mathematical programming model for the optimal design of the global biofuel supply chain under uncertainty. We considered the uncertain factors of price and market demand. This uncertainty complicates the assessment of investment. The supply chain model involves countries in Southeast Asia, Europe, and North America. The raw material used for biodiesel formulation is produced in Southeast Asian planting fields and oil extraction factories. The refinery factory sites, where raw material is transformed into biodiesel, are located in Europe and North America. The resulting product is sold to customers in Europe and North America according to local government policies. This study aimed to determine how to allocate biomass cultivated area in Southeast Asia (i.e., optimal crop portfolio allocation) and to identify the optimal transportation paths under uncertainty to maximize the expected global profit in this supply chain.
This paper is divided into two main sections. The first section analyzes economic benefits and calculates the carbon emissions of biomass types, including soybean, rapeseed, palm trees, jatropha, castor, and waste cooking oil. Identifying valuable biodiesel with high economic benefits and low carbon emissions is useful and contributes to the optimal development of biomass. The second part formulates a two-stage recourse mixed-integer stochastic programing to determine the optimal design of a global biodiesel supply chain model under uncertainty. The first section involves a stage decision regarding optimal capital investment, whereas the second section discusses a stage recourse decision and suggests the optimal transportation flow in each scenario.
|
|---|
