Integrating Analytical Hierarchical Process and Network Optimization Model to Support Decision-Making on Biomass Terminal Selection

Forest biomass is an appealing bioenergy feedstock due its renewability, availability and potential to stimulate local economies. It is, however, voluminous, with heterogenous fuel characteristics and uncertainties in its supply. The feasibility of a bioenergy facility is contingent on a secure supply of uniform feedstock; a terminal in the supply chain can be useful in this regard. Biomass can be treated in the terminal to meet quality specifications and stored to overcome seasonality and supply disruptions. Nonetheless, such terminals require a significant capital investment; thus, the decision to use a terminal needs to be made judiciously. The decision process must account for a diverse set of factors that influence the terminal's effectiveness. These include both quantitative and qualitative factors. The objective of this study is to develop a multi-criteria decision-making framework that takes quantitative and qualitative factors into consideration while selecting a terminal. The framework consists of analytical hierarchy process to analyze qualitative information, and a mixed-integer programming model to evaluate quantitative information including fuel quality (moisture content and thermal value). This hybrid framework was implemented in a case study. It proved to be an effective tool for identifying terminals with the highest potential to generate value for the bioenergy supply chain.

Automated summary

Forest biomass is a promising bioenergy feedstock with renewable and available qualities. However, its voluminous and heterogeneous characteristics, as well as supply uncertainties, pose challenges. The use of terminals can help secure a uniform supply of biomass by treating and storing it. The decision to use a terminal should consider diverse factors that affect its effectiveness. This study develops a multi-criteria decision-making framework that integrates quantitative and qualitative factors to select terminals, proving to be effective in identifying terminals with high potential to generate value for the bioenergy supply chain.