This paper addresses the critical issue of selecting the optimal solar tracking configuration for maximum energy generation, given the increasing demand for sustainable energy solutions in information and communication technology (ICT) facilities. The main goal is to thoroughly evaluate and compare seven different solar tracking configurations across technical, economic, and environmental dimensions: No Tracking (NT), Monthly Adjusted Horizontal Axis (MAHA), Weekly Adjusted Horizontal Axis (WAHA), Daily Adjusted Horizontal Axis (DAHA), Continuously Adjusted Horizontal Axis (CAHA), Continuously Adjusted Vertical Axis (CAVA), and Dual Axis with Continuous Adjustment (DACA). This study utilizes the HOMER simulation program to evaluate its energy and hydrogen production, emissions, and cost-effectiveness performance. Key findings indicate solar tracking improves energy efficiency, with optimal capacity factors of 18.2% and 17.7% for CAHA and DAHA configurations, respectively. Although load-following strategies increase reliability, there is a trade-off between capital costs and energy costs. In addition, an MCDM approach helps to consolidate the evaluation, resulting in CAVA being ranked as the most preferable option. The study contributes to informed decision-making for energy systems in ICT facilities by emphasizing the significance of considering a variety of criteria and evaluation techniques to address complex energy challenges.

This research integrates stochastic optimization techniques with robust modeling and probabilistic modeling approaches to enhance photovoltaic cell manufacturing processes and product reliability. The study employed an adapted genetic algorithm to tackle uncertainties in the manufacturing process, resulting in improved operational efficiency. It consistently achieved optimal fitness, with values remaining at 1.0 over 100 generations. The model displayed a dynamic convergence rate, demonstrating its ability to adjust performance in response to process fluctuations. The system preserved resource efficiency by utilizing approximately 2600 units per generation, while minimizing machine downtime to 0.03%. Product reliability reached an average level of 0.98, with a maximum value of 1.02, indicating enhanced consistency. The manufacturing process achieved better optimization through a significant reduction in defect rates, which fell to 0.04. The objective function value fluctuated between 0.86 and 0.96, illustrating how the model effectively managed conflicting variables. Sensitivity analysis revealed that changes in sigma material and lambda failure had a minimal effect on average reliability, which stayed above 0.99, while average defect rates remained below 0.05. This research exemplifies how stochastic, robust, and probabilistic optimization methods can collaborate to enhance manufacturing system quality assurance and product reliability under uncertain conditions.