AN IMPROVED HYBRID MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION FRAMEWORK FOR GREEN WAREHOUSE STORAGE ALLOCATION IN SMART LOGISTICS

Green warehouse management has become an essential component of sustainable logistics due to the increasing demand for reducing operational costs and environmental impacts. Among warehouse management tasks, storage allocation significantly affects travel distance, energy consumption, and carbon emissions. Existing optimization approaches often focus on a single objective or fail to balance economic and environmental performance effectively.
This paper presents a multi-objective optimization framework based on Particle Swarm Optimization
(MOPSO) to address the green warehouse storage allocation problem. A mathematical model is developed to simultaneously minimize the total operating cost and carbon emissions while satisfying warehouse capacity, storage compatibility, and operational constraints. The proposed framework generates a diverse set of Pareto-optimal solutions, enabling warehouse managers to analyze trade-offs between economic efficiency and environmental sustainability.
Simulation experiments are conducted in MATLAB under various warehouse scenarios. The optimization results are evaluated using Pareto-front analysis, heatmaps, and Monte Carlo simulations to investigate solution quality and robustness. Experimental results demonstrate that the proposed framework effectively balances operational cost reduction and carbon emission mitigation, providing practical decision support for sustainable warehouse management.