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

TL;DR

This paper introduces an adaptive ensemble differential evolution algorithm enhanced with simulation-based hybridization and self-adaptation, aimed at improving inventory management under uncertainty by handling complex stochastic demand scenarios.

Contribution

It presents a novel hybrid algorithm combining differential evolution with simulation-based self-adaptation for robust inventory management in uncertain environments.

Findings

Improves financial performance of inventory management systems.

Effectively handles large search spaces and demand uncertainty.

Demonstrates robustness through sensitivity analysis.

Abstract

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.