To determine the effectiveness of metaheuristic Differential Evolution optimization strategy for inventory management (IM) in the context of stochastic demand, this empirical study undertakes a thorough investigation. The primary objective is to discern the most effective strategy for minimizing inventory costs within the context of uncertain demand patterns. Inventory costs refer to the expenses associated with holding and managing inventory within a business. The approach combines a continuous review of IM policies with a Monte Carlo Simulation (MCS). To find the optimal solution, the study focuses on meta-heuristic approaches and compares multiple algorithms. The outcomes reveal that the Differential Evolution (DE) algorithm outperforms its counterparts in optimizing IM. To fine-tune the parameters, the study employs the Latin Hypercube Sampling (LHS) statistical method. To determine the final solution, a method is employed in this study which combines the outcomes of multiple independent DE optimizations, each initiated with different random initial conditions. This approach introduces a novel and promising dimension to the field of inventory management, offering potential enhancements in performance and cost efficiency, especially in the presence of stochastic demand patterns.

本实证研究旨在确定元启发式差分进化优化策略在随机需求背景下的库存管理领域的有效性，并通过深入调查揭示最有效的策略以最小化不确定需求模式下的库存成本。结果显示，差分进化算法在优化库存管理方面优于其他算法，同时探讨了拉丁超立方抽样等参数调优方法。该研究引入了一种新颖且有前景的方法，为库存管理领域带来了潜在的性能和成本效益的提升，特别是在存在随机需求模式的情况下。

应对库存管理中需求的不确定性和变异性的多个独立差分进化优化