An important challenge of dataflow program implementations on multi-core platforms is the partitioning and scheduling providing the best possible throughput when satisfying multiple objective functions. Not only it has been proven that these problems are NP-complete, but also the quality of any heuristic approach can be affected by other factors (e.g., buffer dimensioning, influence of an established partitioning configuration and scheduling strategy on each other, uncertainties of a compiler affecting the profiling information). This paper proposes an evaluation of alternative partitioning and scheduling configurations based on the application profiling results. It investigates the impact of the scheduling on the overall execution time, and verifies which policies could further drive the metaheuristic-based search of a close-to-optimal partitioning configuration.