عنوان مقاله [English]
Reducing dependence on fossil fuels and environmental pollution is one of the most important incentives to produce fuels from renewable energy. Nowadays, renewable resources are being considered for reasons such as economic and environmental benefits and easy access. They are used to generate electricity and clean fuels and heat. In recent years, biomass is considered as a renewable source, and its use is rapidly growing .Biofuels derived from biomass can play a key role nowadays as one of the main sources of renewable energies. Therefore, more and more researchers have been involved in modeling and optimizing biomass supply chains. Lignocellulosic biomass is a rich and
renewable natural resource composed of cellulose, hemicellulose, and lignin. This source can replace fossil fuels to produce biofuels without compromising food security. Agricultural wastes are among the sustainable sources of lignocellulosic biomass, and a million tons of agricultural waste is produced, which is one of the major sources of biofuels. One of the obstacles to the use of these renewable sources is the cost associated with the supply chain such as transportation and production costs which are among the important costs in the supply chain. In this research, a single-objective, multi-level and multi-period linear programing under uncertainty with chance constraints is presented to maximize the profit, in which hub is used as an intermediate level. Hub facilitates the transmission of biomass between supply chain levels. In the proposed mathematical model, lignocellulosic biomass was used to generate bioethanol and lignin. Sales of lignin as by-products and multimodal transportation represent other ways to reduce costs. After sensitivity analysis, the results showed that increasing the sales price more than reducing transportation costs and increasing demand had a positive effect on the profitability of the entire supply chain and the proposed model was economically justifiable.