Goal programming

Recall the standard RO form below

\[\begin{split}\begin{split} \text{min} &~~f_0(x) \\ \text{s.t.} &~~\underset{u}{\text{max}}~f_i(x,u) \leq 0,~i = 1,\ldots,n \\ &~~\left\lVert u \right\rVert \leq \Gamma \\ \end{split}\end{split}\]

where we attempt to minimize \(f_0\) for the worst-case realization of uncertain parameters in the set. We can flip this on its head, and solve the following problem

\[\begin{split}\begin{split} \text{max}~~\Gamma \\ \text{s.t.}~~f_i(x,u) &\leq 0,~i = 1,\ldots,n \\ \left\lVert u \right\rVert &\leq \Gamma \\ f_0(x) &\leq (1+\delta)f_0^*,~\delta \geq 0 \end{split}\end{split}\]

where \(f_0^*\) is the optimum of the nominal problem and \(\delta\) is a fractional penalty on the objective that we are willing to sacrifice for robustness, which gives \((1+\delta)f_0^*\) as the upper bound on the objective value.

The benefit of this goal programming form is that we can start to use risk as a global design variable against with all objectives can be weighed.


To use the goal programming functions in robust, you can use the simulate.variable_goal_results function, which has the same inputs as simulate.variable_gamma_results except for having the penalty parameter \(\delta\) instead of the uncertainty set size \(\Gamma\) as its input.

What occurs to the solved nominal model under the hood is the following:

Gamma = Variable('\\Gamma', '-', 'Uncertainty bound')
delta = Variable(value, '1+\\delta', '-', 'Acceptable optimal solution bound', fix = True)
origcost = model.cost
mGoal = Model(1 / Gamma, [model, origcost <= Monomial(nominal_solution(origcost)) * delta, Gamma <= 1e30, delta <= 1e30],
robust_goal_model = RobustModel(mGoal, uncertainty_set, gamma=Gamma)
sol = robust_goal_model.robustsolve()

This is an exact formulation of the aforementioned risk minimization problem!