Content deleted Content added
Adam Rooney (talk | contribs) No edit summary |
Adam Rooney (talk | contribs) No edit summary |
||
Line 13:
As fuzzy mathematical programming is categorized into ''Possibilistic programming'' and ''Flexible programming'', ROFP also can be classified into (see Pishvaee and Fazli, 2016):
2) '''Robust flexible programming (RFP)'''
3) '''Mixed possibilistic-flexible robust programming (MPFRP)'''
The first category is used to deal with imprecise input parameters in optimization problems while the second one is employed to cope with flexible constraints and goals. Also, the last category is capable to handle both uncertain parameters and flexibility in goals and constraints.
From another point of view, it can be said that different ROFP models developed in the literature can be classified in three categories according to degree of conservatism against uncertainty. These categories include:
2) '''soft worst case ROFP'''
3) '''realistic ROFP'''
Hard worst case ROFP has the most conservative nature among ROFP methods since it provides maximum safety or immunity against uncertainty. Ignoring the chance of infeasibility, this method immunizes the solution for being infeasible for all possible values of uncertain parameters. Regarding the optimality robustness, this method minimizes the worst possible value of objective function (min-max logic). On the other hand Soft worst case ROFP method behaves similar to hard worst case method regarding optimality robustness, however does not satisfy the constraints in their extreme worst case. Lastly, realistic method establishes a reasonable trade-off between the robustness, the cost of robustness and other objectives such as improving the average system performance (cost-benefit logic).
Line 32:
ROFP is successfully implemented in different practical application areas such as
* [['''
* '''Healthcare management'''
* [['''Energy planning'''
to handle epistemic uncertainty of input parameters and flexibility of goals and constraints.
| |||