Development of an Enhanced Supply Chain Algorithm for ERP Systems Using ACO, Genetic, and Floyd-Warshall Algorithms
DOI:
https://doi.org/10.9734/bpi/erpra/v3/3322Keywords:
Supply chain optimization, ERP systems, ant colony optimization (ACO), genetic algorithms, Floyd-Warshall algorithm, dynamic routing, lead time analysis, weather impact, delivery history, item receipts, cost efficiency, algorithmic comparison, logistics, data-driven decision making, simulationAbstract
With the increasing complexity of global supply chains, there's a pressing need for advanced algorithms that can optimize routes, reduce costs, and ensure timely deliveries. In the era of digital transformation, optimizing supply chains is paramount for businesses to remain competitive. This research article delves into the creation of an enhanced supply chain algorithm for Enterprise Resource Planning (ERP) systems using the Ant Colony Optimization (ACO), Genetic, and Floyd-Warshall algorithms. Through a comparative analysis using dummy data from two companies, Alco and Palto, the efficacy of the study approach was demonstrated. The integration of Ant Colony Optimization (ACO), Genetic, and Floyd-Warshall algorithms offers a comprehensive solution for enhancing supply chain operations in ERP systems. While each algorithm has its strengths, the Genetic Algorithm proved to be particularly effective in the simulation due to its ability to consider multiple variables simultaneously.
