This project is a multi-disciplinary effort to integrate two complementary fields of Computer Science, namely fuzzy regression analysis and constraint-based reasoning, in order to develop decision support systems for optimization problems permeated with measurement uncertainty.Data uncertainty due to imprecise or incomplete measurements is ubiquitous in many real world applications. Recently, models derived from fuzzy regression analysis have been defined to represent incomplete and imprecise measurements. They are mostly used in complex systems analysis seeking a correlation between crisp or fuzzy measurements.The Constraint Programming (CP) paradigm has proved successful to tackle decision and optimization problems in planning and resource optimization. Interestingly, it handles some forms of uncertainty, but has not, to our knowledge, been enhanced to deal with fuzzy regression models. Our objectives are threefold: 1) to study the theoretical aspect of integrating two complementary paradigms, 2) to design and implement an interval regression constraint system, and apply it to a case study in the field of Renewable Energies (RE) techno-economics. The decision support prototype will tackle the problem of RE portfolio optimization for short and longer term, 3) to disseminate our results at the EU and International levels.The objectives will be achieved by combining the host expertise in fuzzy regression models, and the applicant’s expertise in CP languages and optimization techniques, and her more recent works in RE techno-economics problems in Egypt. A set of three activities matching the objectives will be carried out divided into survey, design and implementation work packages and clear milestones. This project is relevant to CIG Work Programme for 3 main reasons: 1) it is a multi-disciplinary project, 2) expertise will be transferred and acquired, 3) dissemination and re-integration will enhance EU research excellence already strong in these domains.