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：本研究针对不确定性环境下的库存管理问题，提出了一种集成差分进化与基于仿真的混合及自适应方法（EDESH-SA）。该方法将多轮运行的差分进化算法与基于仿真的混合技术相结合，通过引入自适应机制，根据每次迭代的成功与否动态调整变异率和交叉率。凭借其自适应性，该算法能够有效处理库存管理中的复杂性和不确定性。利用蒙特卡罗仿真，在考虑随机性和多种需求场景的情况下，对连续盘点库存策略进行了分析。这种基于仿真的方法使得算法在解决实际库存管理挑战中的适用性得到了真实评估。实证结果表明，该方法有望提升库存管理的财务绩效并优化大规模搜索空间。研究采用Ackley函数进行性能测试，并通过扰动敏感性分析探讨变量变化对目标值的影响，为算法的行为特征和鲁棒性提供了有价值的见解。