Optimisation under uncertainty
In planning problems, many parameters are based on forecasts, which introduce uncertainties. These can be addressed using methods such as robust or stochastic optimisation. However, integrating uncertainties leads to more complex models and longer computation times, which in turn increases the need for efficient modeling and solution techniques.
Improved modeling of complex nonlinear relationships
Many optimisation problems involve nonlinear relationships that must be considered. Directly including these in the model often requires computationally demanding methods to achieve an optimal solution. In such cases, it is important to streamline the handling of nonlinear relationships, for example through approximation using linear relationships.
