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.

翻译：为确定元启发式差分进化优化策略在随机需求背景下对库存管理的有效性，本实证研究开展了系统性探究。核心目标在于甄别不确定性需求模式下的最优库存成本最小化策略。库存成本指企业持有与管理库存所产生的相关费用。研究方法采用库存管理策略的持续审查机制与蒙特卡洛模拟相结合。为寻求最优解，本研究聚焦元启发式方法，并对多种算法进行了横向比较。结果表明，差分进化算法在库存管理优化中表现优于其他算法。为优化参数设置，研究采用拉丁超立方抽样统计方法。最终解决方案通过整合多次独立差分进化优化的结果（每次优化均采用不同随机初始条件）得以确定。该方法为库存管理领域开辟了创新且富有前景的研究维度，尤其在随机需求模式下，可显著提升运营绩效与成本效益。