rences in microwave connected network [28]. Consequently, the bandwidth of both substrate and virtual links could be uncertain (stochastic). To capture this uncertainty, we use the stochastic variables bs and by to replace the corresponding coefficients bs and by in formulation (10.7) respectively. We assume that by follows a uniform distribution inside a symmetric interval which centers on bs with perturbation of by Similarly, we also assume that another uniform distribution inside a symmetric interval centers on bk with perturbation of by Note that we allow In what follows, we first formulate the MIP with stochastic variables. We aim to leverage the robust optimization technique [18] to solve it. Based on robust optimization, we then transform the MIP with stochastic variables into a computational tractable form. We assume that virtual or substrate link uncertainties only affect the elements of the coef- ficient matrix. Let H represent the coefficient matrix of the optimization problem in (10.20) considering the existence of stochastic variables. H is dimensional matrix. We denote the component of H by hij, whose perturbation and mean value are hij and hij respectively. Remember that hij is the component of H in (10.20). hij follows a uniform distribution inside a symmetric interval [hi-hith The FRP with stochastic variables is formulated as follows: (10.23) Hx à, (10.24) (10.25) Unfortunately, we cannot solve problem (10.23) directly due to the stochastic variables. To cope with this problem, we need to transform (10.23) into its robust counterpart [18]. The robust counterpart is a solution method by creating a deterministic equivalent, which is computationally tractable, to deal with an uncertain problem like (10.23). It should be men- tioned that in each row of H, there will be at most one stochastic variable (either or In other words, for the constraint containing one stochastic variable of either bs or we only need to introduce one intermediate variable (denoted by yij) to transform such constraint 10 Robu