Development of an Enhanced Supply Chain Algorithm for ERP Systems Using ACO, Genetic
0Development of an Enhanced Supply Chain Algorithm for ERP Systems Using ACO, Genetic, and Floyd-Warshall Algorithms
Layman Abstract: As global supply chains grow more complex, businesses need better ways to manage their routes, reduce costs, and ensure products are delivered on time. This research looks into improving supply chain management by creating a smarter algorithm for Enterprise Resource Planning (ERP) systems. The study combines three powerful algorithms: Ant Colony Optimization (ACO), Genetic Algorithm, and Floyd-Warshall algorithm. Using test data from two companies, Alco and Palto, the study shows how these algorithms can help optimize supply chains. While all three have benefits, the Genetic Algorithm stood out as the most effective for considering multiple factors at once in the simulation.
Original Abstract: 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.
View Book:- https://doi.org/10.9734/bpi/erpra/v3/3322
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