Exact methods, such as the branch-and-bound method and Lagrangian decomposition method, highly rely on the model mathematical properties.
Even though the performance of exact methods such as branch and bound is superior to heuristic and meta-heuristic techniques, exact methods fail to solve large size problems.
Exact methods such as B&B fail to produce best results for large size FFSP problems.
Computational experiments show that it is very hard to solve the problem by exact methods.
In any case, only small-size instances can be handled by exact methods.
Confidence intervals (CIs) for risk ratios were calculated by exact methods.
Ninety-five perconfidencedence intervals will be computed by exact methods based on the binomial distribution.
P value was calculated by Exact Method.
This makes standard exact inference methods, such as variable elimination or clique tree propagation (Pearl 1988), extremely time consuming.
The exact expression of P u * is not easily obtained and can be found by the numerical methods, such as the bisection method.
