Ensemble Differential Evolution with Simulation-Based Hybridization and Self-Adaptation for Inventory Management Under Uncertainty
Sarit Maitra, Vivek Mishra, Sukanya Kundu

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…
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Taxonomy
TopicsSupply Chain and Inventory Management · Forecasting Techniques and Applications · Stock Market Forecasting Methods
