Optimizing ESP Component Supply Chains Using Genetic Algorithms: A Novel Approach to Enhancing Operational Efficiency and Cost Reduction in the Oil & Gas Industry

This paper presents a novel approach to optimizing the supply chain management of Electrical Submersible Pump (ESP) components in the oil and gas industry using Genetic Algorithms (GA). The study develops a GA-based framework that dynamically adjusts inventory levels, ordering schedules, and logistics for ESP components to achieve optimal operational efficiency and cost savings without compromising the availability of crucial parts. By incorporating critical factors such as lead times, safety stock levels, and demand variability, the proposed model offers a robust and flexible solution for minimizing costs and ensuring component availability in the dynamic and uncertain environment of oil and gas operations.