REVIEW OF MULTI-VENDOR, MULTI-RETAILER INVENTORY WITH TWO TUNED METAHEURISTIC ALGORITHMS

Abstract

In the evolving landscape of supply chain management, optimizing inventory across multi-vendor, multi-retailer systems presents significant challenges due to the complexity and variability inherent in these networks. Traditional inventory models often fall short in effectively managing these systems, leading to inefficiencies and increased costs. This review explores the application of two tuned metaheuristic algorithms designed to optimize inventory management in such complex environments. These algorithms are tailored to handle the specific challenges posed by multi-vendor, multi-retailer systems, including coordinating inventory levels across multiple stakeholders, reducing holding costs, and minimizing shortages. Through a comprehensive analysis of existing literature and recent studies, the review highlights the strengths and limitations of these algorithms, offering insights into their practical implementation. The findings suggest that when properly tuned, these metaheuristic algorithms can significantly enhance inventory performance, providing a valuable tool for businesses seeking to optimize their supply chain operations. This review contributes to the growing body of knowledge on advanced inventory management strategies and their application in complex, multi-stakeholder supply chain networks.