Cost Effective Agriculture Supply Chain Optimization using Evolutionary Algorithms

Abstract

Amidst concerns about food security and rising inflation, optimizing agriculture supply chains is gaining popularity as a cost-reduction measure. Given this industry's intricate and dynamic nature, effectively managing agricultural supply chains is crucial to sustain food security and ensure the delivery of high-quality products cost-effectively. However, it can take much work to optimize supply networks for agriculture. Evolutionary algorithms have emerged as valuable tools to overcome these challenges and improve various aspects of agricultural supply chains. This study employs evolutionary algorithms to optimize agrarian supply chains and enhance cost-effectiveness. Its primary objective is to minimize operating expenses while ensuring the delivery of high-quality products and maintaining efficient delivery systems. The research comprehensively considers all stages of the agricultural supply chain, including production, processing, storage, transportation, and distribution. The proposed technique involves studying and optimizing the agricultural supply chain using various evolutionary algorithms, such as Genetic Algorithm (GA), Red Deer Algorithm (RDA), and Social Engineering Optimization Algorithm (SEO). These algorithms utilize the principles of natural selection and evolutionary processes to address complex supply chain optimization problems. The research also explores the integration of these evolutionary algorithms with critical decision variables, such as order allocation, inventory control, and transportation routing. The results of this research will provide invaluable insights for designing and implementing costeffective agricultural supply chain systems in practice. Farmers, distributors, and other stakeholders can employ optimized supply chain models to address their challenges, increase efficiency, improve productivity, and reduce costs.