This article addresses the system reliability optimization problem as reliability–redundancy allocation problem, aiming to maximize the system reliability through a trade-off between redundancy levels and the reliability of the components. In this study, cold-standby strategy has been considered for the redundant components, and a population-based meta-heuristic algorithm, called stochastic fractal search, is applied to solve different benchmark problems. Using the proposed stochastic fractal search algorithm, all the benchmark problems are improved and new structures with higher reliability values have been found. The experimental results reveal the superiority of the proposed stochastic fractal search algorithm in terms of quality and robustness of the solutions in cold-standby redundancy case compared to all previous studies.

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We develop a multistage portfolio optimization model that utilizes options for mitigating market risk in a dynamic setting. Due to the key role of scenarios in the quality of investment decisions, a new scenario generation method is proposed that characterizes the dynamic behavior of asset returns. This methodology takes the dependence structure of different asset returns into account, and also considers serial correlations of each of the asset returns. Moreover, it preserves marginal distributions of asset returns. Also, it precludes arbitrage opportunities. To investigate the role of options, we implement the scenario generation method on a set of stocks selected from the New York Stock Exchange. Results show the high performance of the proposed scenario generation method. Afterwards, the generated set of scenarios is used as the uncertainty set for the multistage portfolio optimization model. Static and dynamic assessments are used for measuring the performance of options in mitigating market risks and generating additional returns. Finally, backtesting simulations are used for assessing different trading strategies of options.

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