Intelligent algorithm approaches that augment the analytical capabilities of traditional techniques may improve the evaluation and performance of decision making units (DMUs). Crises such as the massive COVID-19 pandemic-related shock to businesses have prompted the deployment of analytical tools to provide solutions to emerging complex questions with incredible speed and accuracy. Performance evaluation of DMUs (e.g., financial institutions) is challenging and often depends on the sophistication and robustness of analytical methods. Therefore, advances in analytical methods capable of accurate solutions for competitive real-world applications are essential to managers. This dissertation introduces and reviews three robust methods for evaluating and optimizing the decision-making processes of DMUs to assist managers in enhancing the productivity and performance of their operational goals. The first essay proposes a robust search field division method, which improves the performance of evolutionary algorithms. The second essay proposes a robust double judgment approach method that enhances the efficiency of the data envelopment analysis method. The third essay proposes a robust general regression neural network method to examine the effect of shocks on GDP loss caused by COVID-19 on the global economy. These three essays contribute to optimization methodology by introducing novel robust techniques for managers of DMUs to improve the evaluation and performance of their units as well as by providing guidelines in selecting appropriate models to improve solutions to real-world optimization problems.