Ensemble Differential Evolution with Simulation-Based Hybridization and Self-Adaptation for Inventory Management Under Uncertainty

Abstract

This study proposes an Ensemble Differential Evolution with SimulationBased Hybridization and SelfAdaptation (EDESHSA) approach for inventory management (IM) under uncertainty. In this study, DE with multiple runs is combined with a simulationbased hybridization method that includes a selfadaptive 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 examined while accounting for stochasticity and various demand scenarios. This simulationbased approach enables a realistic assessment of the proposed algorithm’s applicability in resolving the challenges faced by IM in practical settings. The empirical findings demonstrate the potential of the proposed method to improve 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. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.