The literature review of scheduling in manufacturing environments reveal that job processing times are mainly modelled as known fixed values. This might be true for some manufacturing environments, however, it is not true for some other manufacturing environments. Hence, job processing times have to modelled as uncertain variables. We consider flowshop scheduling problem with the objective function of minimizing mean completion time in this paper where processing times are modelled as uncertain random variables. Given that the problem is NP-hard, five heuristics are proposed and their performances are compared based on randomly generated data. Computational experiments reveal that one of the heuristics performs very well, and hence, it is recommended.