Sekamääräohjelmointi

Sekamääräohjelmointi, also known as randomized programming or stochastic programming, is a branch of optimization that deals with decision problems where some of the data are not known with certainty but are described by probability distributions. The goal of sekamääräohjelmointi is to find a decision or a sequence of decisions that optimizes some objective function, typically minimizing expected costs or maximizing expected profits, given the inherent uncertainty.

This approach is particularly useful in situations where the outcomes of decisions are subject to random fluctuations, such as in finance, operations research, and resource management. For example, in financial portfolio optimization, sekamääräohjelmointi can be used to determine the best allocation of assets under uncertain market conditions. In supply chain management, it can help in deciding inventory levels when demand is uncertain.

The formulation of a sekamääräohjelmointi problem typically involves defining decision variables, an objective function to be optimized (often an expectation), and constraints that must be satisfied under various possible realizations of the uncertain parameters. The uncertain parameters are represented by probability distributions, and the problem is solved by considering these distributions.

Solving sekamääräohjelmointi problems can be computationally challenging due to the need to incorporate probabilistic information. Various techniques are employed, including scenario analysis, approximation methods, and specialized algorithms. Despite the complexity, sekamääräohjelmointi provides a powerful framework for making robust decisions in the face of uncertainty.