This master s thesis analyzes the Permutation Flow-shop Scheduling Problem with Delivery Dates and Cumulative Payoffs (whenever these dates are met) under uncertainty conditions. In particular, the work considers the realistic situation in which processing times and release dates are stochastics. The main goal is to find the permutation of jobs that maximizes the expected payoff. In order to achieve this goal, first a biased-randomized heuristic is proposed for the deterministic version of the problem. Then, this heuristic is extended into a metaheuristic by encapsulating it into a variable neighborhood descent framework. Finally, the metaheuristic is extended into a simheuristic by incorporating Monte Carlo simulation. According to the computational experiments, the level of uncertainty has a direct impact on the solutions provided by the simheuristic. Moreover, a risk analysis is performed using two well-known metrics: the value at risk and the conditional value at risk.