A Model for Selecting a Biomass Furnace Supplier Based on Qualitative and Quantitative Factors

In developing countries, solar energy is the largest source of energy, accounting for 35%–45% of the total energy supply. This energy resource plays a vital role in meeting the energy needs of the world, especially in Vietnam. Vietnam has favorable natural conditions for this energy production. Because it is hot and humid, and it has much rainfall and fertile soil, biomass develops very quickly. Therefore, byproducts from agriculture and forestry are abundant and continuously increasing. However, byproducts that are considered natural waste have become the cause of environmental pollution; these include burning forests, straw, and sawdust in the North; and rice husks dumped into rivers and canals in the Mekong Delta region. Biomass energy is provided in a short cycle, is environmentally safe to use and is encouraged by organizations that support sustainable development. Taking advantage of this energy source provides energy for economic development and ensures environmental protection. Due to the abovementioned favorable conditions, many biomass energy plants are being built in Vietnam. Like other renewable energy investment projects, the selection of the construction contractor, the selection of equipment for the installation of the power plant, and the choice of construction site are complex multi-criteria decisions. In this case, decision-makers must evaluate many qualitative and quantitative factors. These factors interact with each other and it is difficult to use personal experience to choose the optimal solution for such complex decision-making problems, especially in a fuzzy decision-making environment. Therefore, in this study, the authors use a Multi-Criteria Decision-Making (MCDM) model that uses a Fuzzy Analytic Hierarchy Process (FAHP) model and the Combined Compromise Solution (CoCoSo) algorithm to select biomass furnace suppliers utilizing both qualitative and quantitative factors. Furthermore, the results of this work will provide the first look at a hybrid CoCoSo/FAHP method that decision-makers in other fields can use to find the best supplier.