The Robust Cold Standby Redundancy Allocation in Series-Parallel Systems with Budgeted Uncertainty
Mohammad Javad Feizollahi (feizollahigatech.edu)
Abstract: This paper studies a redundancy allocation problem (RAP) with cold standby strategy in series-parallel systems. It is assumed that components' reliabilities are uncertain values in a budgeted uncertainty set, with unknown probability distributions. Because the system reliability is a nonlinear function of components' reliabilities, classical robust optimization approaches cannot be applied directly to construct robust optimization counterpart of this problem. To deal with budgeted uncertainty in this problem, by exploiting problem structure, a robust optimization approach and two exact solution methods are proposed for the first time. One method solves a mixed integer programming (MIP) model iteratively in a Benders' decomposition framework. The other one solves a single binary linear model. The validity and the performance of the proposed approach are tested through Monte Carlo simulation and computational results.
Keywords: Cold standby redundancy allocation, robust optimization, budgeted uncertainty, mixed integer nonlinear programming, series-parallel system.
Category 1: Robust Optimization
Category 2: Integer Programming ((Mixed) Integer Nonlinear Programming )
Citation: Feizollahi M. J., Soltani R., Feyzollahi H. “The Robust Cold Standby Redundancy Allocation in Series-Parallel Systems with Budgeted Uncertainty,” IEEE Transactions on Reliability, 2015.
Entry Submitted: 05/18/2014
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