Resource-constrained scheduling problems appear at different levels of decisions in logistics, manufacturing, computer networks, software engineering etc. They are usually characterized by many types of constraints and decision variables which often make them difficult to solve (NP-complete). In addition, these problems are often characterized by the uncertainty of resources, allocations and time. Opportunity to ask questions and get answers about the feasibility/optimality of a schedule in uncertain conditions (e.g. about available resources) is extremely important for decision-makers. This paper presents a hybrid approach to modeling and solving robust constrained scheduling problems where two environments (mathematical programming and constraint logic programming) were integrated. This integration, hybridization as well as a transformation of the problem helped reduce the combinatorial problem substantially. In order to compare the effectiveness of the proposed approach to the mathematical programming approach, illustrative example was implemented in both environments for the same data instances.