Optimization of Green Warehouse Layout Considering Energy Imbalance Using a Genetic Algorithm

The efficient design of warehouse layouts plays a crucial role in reducing logistics costs and energy consumption, particularly in modern green warehouses where energy imbalance is a significant challenge. This study presents a dual-objective optimization model that minimizes both material handling costs and energy fluctuations using a Genetic Algorithm (GA). Unlike conventional models that focus solely on logistics efficiency, this approach integrates energy-aware optimization, ensuring an even distribution of energy intensive functional areas. The results indicate a ۲۱% reduction in operational costs and a more balanced energy consumption profile across warehouse sections. The GA-based optimization demonstrated rapid convergence, achieving optimal layout configurations within ۱۵ generations. The findings validate that structured initialization and heuristic optimization significantly enhance warehouse performance. This study contributes to the development of sustainable warehouse planning, offering a scalable solution for energy-efficient logistics systems. Future research can explore real-time adaptability and hybrid AI-driven optimization techniques for further advancements.

Keywords:

Carbon footprint , Layout optimization , Green Warehouse , Energy imbalance , Genetic Algorithm