In decision tasks, imprecision and uncertainty can arise from many sources such as data uncertainty, data reliability, or the necessity to use intermediate (non-fully reliable) models. User preferences may also be included into the decision process, making it even more complex. This is particularly true for processes involving biological materials. Soft computing methods have the potential to be the kingpin of specialized software that can be integrated in decision support systems intended to solve the mentioned issues. This work presents three such methods and their implementation: (i)-simulation module with uncertain inputs and interval analysis, (ii)-reliability module allowing to rank candidates according to expert criteria, and (iii)-bipolar flexible querying module, that makes the difference between constraints and wishes in a data base query. A decision support system architecture is proposed and illustrated on a case study, dealing with biodegradable and biosourced packaging design.