Linear Decision Models Under Risk.
CALIFORNIA UNIV BERKELEY OPERATIONS RESEARCH CENTER
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The assumption made in linear programming that the components are Deterministic constant numbers is rarely fulfilled in practical applications. This has led to the development of the field of stochastic programming where the random aspect of the coefficients in the objective function, technology matrix, and the vector of resources are taken into account. This research investigates the problem of a linear program with uncertainty attached to the decision vector. For example, a decision to order a certain amount of a perishable good might yield variable amounts of this good at delivery due to spoilage.
- Statistics and Probability