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Recent development methodologies from the industry and the academia for complex real-time systems define a stage in which system functions are deployed onto an execution platform. The deployment consists of the placement of functions on a distributed network of nodes, the partitioning of functions in tasks and the scheduling of tasks and messages. In this paper, we present two approaches towards the efficient deployment of realistic and complex real-time systems by considering tree-shaped functional models. A formal approach to compute optimal deployment and a heuristic approach to scale to industry-size systems. The approaches consider placement, partitioning and scheduling, and are based on mixed integer linear programming (MILP) technique. Furthermore, we present a deep evaluation of the proposed deployment approaches to show the benefits and limits of a MILP-based deployment approach. A set of synthetic use-cases as well as a real-life automotive system are used to assess the quality and scalability of our deployment approaches. Considering use-cases, we show an added value with respect to end-to-end latencies optimization when solving the three stages of the deployment problem at the same time. This is done by comparing the quality of the solutions obtained with our techniques to those returned by the existing approaches. [ABSTRACT FROM AUTHOR]