Chance Constrained Programming (CCP) is a mathematical optimization approach used to handle problems under uncertainty.
[1][2] CCP is widely used in various fields, including finance, engineering, and operations research, to optimize decision-making processes where certain constraints need to be satisfied with a specified probability.
Chance Constrained Programming involves the use of probability and confidence levels to handle uncertainty in optimization problems.
There are different approaches depending on the nature of the problem: Chance constrained programming is used in engineering for process optimisation under uncertainty and production planning and in finance for portfolio selection.
[3] It has been applied to renewable energy integration,[4] generating flight trajectory for UAVs,[5] and robotic space exploration.
For example, in optimizing the design and operation of chemical plants, CCP helps in achieving desired performance levels while accounting for uncertainties in feedstock quality, demand, and environmental conditions.
A typical problem formulation involves maximizing profit while ensuring that production constraints are satisfied with a certain probability.
This approach allows investors to consider the uncertainty in asset returns and make more informed investment decisions.