To compete with fossil fuel sourced energy production systems in today's highly global and competitive markets, decision models for bioenergy supply chain planning should be developed to represent the real world problem precisely. Unfortunately, in real world, most of the energy system structures and parameters to be included in the models are not exactly known and do not represent definitely the real problem. Uncertainty may be related to target values of objectives, supply and demand, costs, prices etc. Incom- plete, imprecise and/or uncertain parameter values should be incorporated into the modeling approach to represent the energy systems more realistically. Bioenergy supply chain models pro- posed so far, except a few ones, either used traditional crisp modeling approaches that ignore uncertainty or consider it with additional analyses (i.e. sensitivity and scenario analyses) or with stochastic modeling approaches that depends on probability dis- tributions governing the data.