Joint chance-constrained reliability optimization with general form of distributions

• Autores: Vincent, Charles, Islam Ansari, Saifu, Khodabakhshi, Mohammad
• Formato: documento de trabajo
• Fecha de Publicación: 2014
• Institución: Pontificia Universidad Católica del Perú
• Repositorio: PUCP-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.pucp.edu.pe:20.500.14657/166794
• Enlace del recurso: https://repositorio.pucp.edu.pe/index/handle/123456789/166794; http://dx.doi.org/10.7835/ccwp-2014-01-0005
• Nivel de acceso: acceso abierto
• Materia: Chance-constrained programming; Reliability optimization; Joint constraints; General form of distributions; http://purl.org/pe-repo/ocde/ford#5.02.04

Probabilistic or stochastic programming is a framework for modeling optimization problems that involve uncertainty. Stochastic programming models arise as reformulations or extensions of reliability optimization problems with random parameters. Moreover, the resource elements vary and it is reasonable to consider them as stochastic variables. In this paper, we describe the chance-constrained reliability stochastic optimization (CCRSO) problem for which the objective is to maximize the system reliability for the given joint chance constraints where only the resource variables are random in nature and which follow different general form of distributions. Few numerical examples are also presented to illustrate the applicability of the methodology.