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：本研究提出一种面向不确定性库存管理的集成差分进化算法（EDESH-SA），该方法融合了仿真驱动的混合策略与自适应机制。该算法将多轮运行的差分进化与基于仿真的混合方法相结合，通过引入自适应机制，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，该算法能够有效处理库存管理中的复杂性与不确定性。采用蒙特卡洛仿真方法，在考虑随机性与多种需求场景的条件下，分析了连续盘点库存策略。这种基于仿真的方法能够真实评估所提算法在解决实际库存管理挑战中的适用性。实验结果表明，该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，并通过扰动敏感性分析方法探究变量变化对目标值的影响，为揭示算法的行为特性与鲁棒性提供了重要见解。