We compare both deterministic and robust stochastic approaches to the problem of scheduling a set of scientific tasks under processing time uncertainty.
While dealing with strict time windows and minimum transition time constraints, we provide closed-form expressions to compute the exact probability that a solution has to remain feasible.
Experiments, taking uncertainty on the stochastic knowledge itself into account, are conducted on real instances involving the constraints faced and objectives pursued during a recent two-week Mars analog mission in the desert of Utah, USA.
The results reveal that, even when using very bad approximations of probability distributions, solutions computed from the stochastic models we introduce, significantly outperform the ones obtained from a classical deterministic formulation, while preserving most of the solution’s quality.
