Comparative analysis and sensitivity evaluation of metaheuristic algorithms for critical inventory optimization

This study comparatively analyzes the performance of various modern metaheuristic algorithms in addressing the critical stock optimization problem in inventory management. Using a custom-developed software tool, these algorithms were applied to a standardized dataset, and a comprehensive sensitivity analysis was conducted to examine the impact of lead time changes and daily consumption on the optimization results. Findings indicate that certain single-state algorithms demonstrate notable computational efficiency, offering rapid and effective solutions. In terms of overall stock reduction capacity, most algorithms achieved significant decreases in existing stock levels; while some algorithms executed more radical stock reductions, others adopted more conservative approaches. The sensitivity analysis revealed that particular algorithms generated more flexible and robust solutions against uncertainties in lead time and daily consumption, exhibiting less sensitivity to fluctuations in these parameters. Consequently, it is concluded that specific metaheuristic algorithms hold promising potential for developing fast, cost-effective, and efficient inventory policies in such optimization problems. Future research could encompass a more in-depth analysis of cost parameters, explore multi-objective optimization approaches, and investigate the scalability of these algorithms on larger and more complex datasets. Additionally, innovative topics such as real-time data integration and automated algorithm parameter tuning for dynamic inventory management warrant further investigation.