This study proposes an Ensemble Differential Evolution with Simula-tion-Based Hybridization and Self-Adaptation (EDESH-SA) approach for inven-tory management (IM) under uncertainty. In this study, DE with multiple runs is combined with a simulation-based hybridization method that includes a self-adaptive mechanism that dynamically alters mutation and crossover rates based on the success or failure of each iteration. Due to its adaptability, the algorithm is able to handle the complexity and uncertainty present in IM. Utilizing Monte Carlo Simulation (MCS), the continuous review (CR) inventory strategy is ex-amined while accounting for stochasticity and various demand scenarios. This simulation-based approach enables a realistic assessment of the proposed algo-rithm's applicability in resolving the challenges faced by IM in practical settings. The empirical findings demonstrate the potential of the proposed method to im-prove the financial performance of IM and optimize large search spaces. The study makes use of performance testing with the Ackley function and Sensitivity Analysis with Perturbations to investigate how changes in variables affect the objective value. This analysis provides valuable insights into the behavior and robustness of the algorithm.

翻译：本研究提出了一种面向不确定环境下库存管理(IM)的集成差分进化算法，该算法融合基于仿真的混合机制与自适应特性(EDESH-SA)。研究中，将多轮运行的差分进化算法与基于仿真的混合方法相结合，并引入自适应机制——该机制根据每次迭代的成功或失败动态调整变异率与交叉率。凭借其自适应能力，该算法能够有效应对库存管理中的复杂性与不确定性。通过蒙特卡洛仿真(MCS)方法，在考虑随机性与多种需求情景的条件下，对连续盘点(CR)库存策略进行了分析。这种基于仿真的方法能够对算法在实际库存管理挑战中的适用性进行真实评估。实证结果表明，所提方法具有提升库存管理财务绩效并优化大规模搜索空间的潜力。本研究利用Ackley函数进行性能测试，并通过扰动灵敏度分析探究变量变化对目标值的影响，为算法的行为与鲁棒性提供了重要见解。