Optimization is one of the most reliable mathematical tool for decisión making. Since the 50¿s, it is well known that traditional deterministic optimaztion is not appropriate for capturing the uncertain behavior present in most real world applications. Moreover, it was not until the 80¿s when stochastic programming was broad applied in real-world applications. Moreover, it was not until the 80¿s when stochastic programming was broad applied in real-world applications. Uncertainty is the key ingredient in many decision problemas. Financial planing, airline scheduling and unit commitmen in power systems are just few examples of aereas in wich ignoring uncertainty may lead to inferior or simply wrong decisions. There are several ways in wich uncertainty can be formalized and over the past thirty years varioud approaches to optimization under uncertainty have been developed. The fiel of Stochastic programming (SP) appears as a response to the need of incorporating uncertainty in mathematical models. Basically, it deals with mathematical programs in which some parameters are random variables.Índice1. Stochastic linear modeling 2. Algorithms in stochastic linear programming 3. Risk measures 4. stochastic integer programming 5. Applications of stochastic programming
Optimization is one of the most reliable mathematical tool for decisión making. Since the 50¿s, it is well known that traditional deterministic optimaztion is not appropriate for capturing the uncertain behavior present in most real world applications. Moreover, it was not until the 80¿s when stochastic programming was broad applied in real-world applications. Moreover, it was not until the 80¿s when stochastic programming was broad applied in real-world applications. Uncertainty is the key ingredient in many decision problemas. Financial planing, airline scheduling and unit commitmen in power systems are just few examples of aereas in wich ignoring uncertainty may lead to inferior or simply wrong decisions. There are several ways in wich uncertainty can be formalized and over the past thirty years varioud approaches to optimization under uncertainty have been developed. The fiel of Stochastic programming (SP) appears as a response to the need of incorporating uncertainty in mathematical models. Basically, it deals with mathematical programs in which some parameters are random variables.Índice1. Stochastic linear modeling 2. Algorithms in stochastic linear programming 3. Risk measures 4. stochastic integer programming 5. Applications of stochastic programming 