In this work we propose an MILP multiperiod formulation for the optimal design and planning of the Argentinean biodiesel supply chain, considering land competition and alternative raw materials. The country is divided into twenty three regions, each one including existing crops, oil and biodiesel plants and potential ones. The model includes intermediate and final products, i.e., seed, flour, pellets and expellers, oil, pure and blending biodiesel and glycerol. Crop fields, storage and production plants, as well as distribution centers for internal and external markets are also represented. We consider the possibility of sowing energetic crops, such as Jatropha curcas, in marginal areas. The time horizon is of seven years, divided into 84 periods. The mathematical model has been implemented in GAMS providing a powerful decision-making tool that can be applied to other regions or countries by adjusting specific data.
In this paper, we address the design and planning of an integrated ethanol and gasoline supply chain. We assume that the supply chain is composed of harvesting sites, production sites for ethanol, petroleum refineries, distribution centers where blending takes place, and the retail gas stations where different blends of gasoline and ethanol are sold. We postulate a superstructure that combines all the components of the supply chain. We consider different means of transportation that connect the nodes in the superstructure. We model this multiscale design of integrated ethanol and gasoline supply chain as a multiperiod MILP model for given forecasts of demand for different blends over the entire time horizon. In order to identify the regions of the US where investments are needed and the optimal configuration of the network, we first consider a strategic planning model in which gasoline stations are aggregated in the different regions.We then consider a second detailed formulation where regions are disaggregated into gas stations in order to also determine the retrofit projects for the selection of blending pumps over their expected life. A bilevel decomposition algorithm is proposed to solve the detailed model. We illustrate the application of these MILP models with two large-scale problems..
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