Optimizing Multi-Vendor, Multi-Retailer Inventory Using Two Enhanced Metaheuristic Algorithms

Abstract

In the complex landscape of multi-vendor, multi-retailer supply chains, efficient inventory management is essential for minimizing costs and ensuring high service levels. Traditional inventory control methods often fall short in addressing the intricacies of such systems, which involve multiple stakeholders and dynamic interactions. This study explores the optimization of inventory management in multi-vendor, multi-retailer systems through the application of two enhanced metaheuristic algorithms. These algorithms are fine-tuned to navigate the large, complex solution spaces inherent in these systems, optimizing key parameters such as order quantities, reorder points, and replenishment schedules. By comparing the performance of these algorithms, the research aims to identify the most effective approach for achieving cost-efficient inventory management while maintaining service quality. The results demonstrate significant improvements in inventory performance, offering practical insights for businesses looking to enhance their supply chain strategies. This study contributes to the growing body of knowledge on the application of metaheuristics in supply chain optimization and provides a robust framework for managing inventory in complex, multi-stakeholder environments.