Optimization of Energy Management Scheduling via Grasshopper Optimization Algorithm in Industries of Bangladesh

Bangladesh is a developing country characterized by a significant and growing demand for electricity. This study investigates the effectiveness of the Grasshopper Optimization Algorithm (GOA) and the Iteration Method in optimizing energy management scheduling within two key industrial sectors in Bangladesh: steel and fabric industries. Using comprehensive data collected from these industries, the study applies both algorithms exclusively in MATLAB to evaluate their efficiency in reducing energy consumption and operational costs. The findings demonstrate that both GOA and the Iteration Method significantly improve the energy scheduling of industrial appliances, leading to lower costs. Additionally, a modified iteration method was introduced, offering an alternative scheduling solution that further optimizes energy consumption and cost-effectiveness. These insights are vital for industries aiming to enhance energy efficiency and reduce operational expenses through advanced scheduling techniques. By providing a detailed comparison of GOA and the Iteration Method in MATLAB, this research offers valuable guidance for selecting and implementing optimal energy management strategies, which can support policy-making and strategic industrial planning in Bangladesh and similar settings.