Abstract
Abstract The demand for bioethanol has increased owing to the growing environmental and social concerns. Designing an efficient Bioethanol Supply Chain (BSC) is a precondition for commercializing the bioethanol production. In this regard, this paper proposes a comprehensive Decision Support Tool (DST) integrating Artificial Neural Network (ANN), mathematical modeling, and solution approach to carefully design and optimize a sustainable second-generation BSC while considering economic, environmental, and social aspects. A multi-objective mixed-integer linear programming model is developed for the strategic and tactical design of BSC in which the bioethanol demand is predicted using an ANN model. To solve the proposed multi-objective model, a hybrid approach based on Best-Worst Method (BWM) and Torabi-Hassini (TH) approach is proposed to find the most preferred solution regarding the decision maker’s preference towards the objective functions. The applicability of the proposed DST is demonstrated through assessing the impact of two alternative BSC expansion policies on a real case study in Iran. The results demonstrate that adapting an appropriate ethanol distribution policy over a planning horizon could result in a 33% reduction in total costs. Moreover, it is suggested that a 10% increase in the total costs can be beneficial from environmental and social perspectives.
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