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The secretary problem demonstrates a scenario involving optimal stopping theory [1] [2] that is studied extensively in the fields of applied probability, statistics, and decision theory. It is also known as the marriage problem, the sultan's dowry problem, the fussy suitor problem, the googol game, and the best choice problem.
In optimization, robustness features translate into constraints that are parameterized by the uncertain elements of the problem. In the scenario method, [ 1 ] [ 2 ] [ 3 ] a solution is obtained by only looking at a random sample of constraints ( heuristic approach) called scenarios and a deeply-grounded theory tells the user how “robust ...
Economists have studied a number of optimal stopping problems similar to the 'secretary problem', and typically call this type of analysis 'search theory'. Search theory has especially focused on a worker's search for a high-wage job, or a consumer's search for a low-priced good.
The satisfiability problem, also called the feasibility problem, is just the problem of finding any feasible solution at all without regard to objective value. This can be regarded as the special case of mathematical optimization where the objective value is the same for every solution, and thus any solution is optimal.
The different routines in supply-chain optimization have reached mature status and allow companies to gain competitive advantage by increased effectiveness and measurable savings, not only but supply chain optimization can bring in a better quality of clothes, possibly better collaboration, and increase profits. [5]
Portfolio optimization is the process of selecting an optimal portfolio (asset distribution), out of a set of considered portfolios, according to some objective.The objective typically maximizes factors such as expected return, and minimizes costs like financial risk, resulting in a multi-objective optimization problem.
The problem of finding the optimal decision is a mathematical optimization problem. In practice, few people verify that their decisions are optimal, but instead use heuristics and rules of thumb to make decisions that are "good enough"—that is, they engage in satisficing.
Optimization problems can be divided into two categories, depending on whether the variables are continuous or discrete: An optimization problem with discrete variables is known as a discrete optimization , in which an object such as an integer , permutation or graph must be found from a countable set .