Measuring and optimizing system reliability: a stochastic programming approach
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Joshua L. Pulsipher [1] ; Victor M. Zavala [1]
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[1] University of Wisconsin–Madison
University of Wisconsin–Madison
City of Madison, Estados Unidos
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- Localización: Top, ISSN-e 1863-8279, ISSN 1134-5764, Vol. 28, Nº. Extra 3, 2020, págs. 626-645
- Idioma: inglés
- DOI: 10.1007/s11750-020-00550-5
- Enlaces
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Resumen
We propose a computational framework to quantify (measure) and to optimize the reliability of complex systems. The approach uses a graph representation of the system that is subject to random failures of its components (nodes and edges). Under this setting, reliability is defined as the probability of finding a path between sources and sink nodes under random component failures and we show that this measure can be computed by solving a stochastic mixed-integer program. The stochastic programming setting allows us to account for system constraints and general probability distributions to characterize failures and allows us to derive optimization formulations that identify designs of maximum reliability. We also propose a strategy to approximately solve these problems in a scalable manner by using purely continuous formulations.
