In this paper we study the problem of scheduling a stochastic task graph with the objective of minimizing the makespan and maximizing the robustness. As these two metrics are not equivalent, we need a bicriteria approach to solve this problem. Moreover, as computing these two criteria is very time consuming we propose different approaches: from an evolutionary metaheuristic (best solutions but longer computation time) to more simple heuristics making approximations (bad quality solutions but

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... t computation time). We compare these different strategies experimentally and show that we are able to find different approximations of the Pareto front of this bicriteria problem.