Energy-saving optimization model and simulation analysis of green warehouse operation path in low-carbon transformation

Abstract

Under the global "dual carbon" goals, the low-carbon transformation of the logistics industry has become an inevitable trend. As the core nodes of logistics systems, warehouses face significant energy consumption challenges (particularly from inefficient movement of handling equipment), which have become a critical bottleneck hindering green transformation. Path optimization, as the key approach to energy conservation and carbon reduction in warehousing, can achieve precise control of energy consumption and carbon emissions by reducing redundant equipment movements and improving operational coordination efficiency. Addressing existing research limitations, such as neglecting the dynamic characteristics of equipment energy consumption, insufficient optimization accuracy under multi-constraint coupling, and a lack of dynamic scenario adaptability, this paper focuses on energy-saving optimization of green warehouse operation paths through model construction and simulation analysis. First, we define warehouse operation scenarios and constraints, extract core optimization problems under single-device multi-tasking, multi-device coordination, and dynamic environments, and establish a multi-objective energy-saving optimization model with objectives of minimizing total energy consumption, carbon emissions, and operation time, incorporating dynamic energy factors such as equipment start-stop energy consumption and load coupling. Second, given the NP-hard nature of the model, we design an improved metaheuristic algorithm (combining local search mechanisms and adaptive parameter adjustment strategies) to enhance solution efficiency and global optimization capabilities.