A Python-based Algorithm for Production and Inventory Optimization

Abstract

Optimization challenges in industrial engineering, particularly in economic order quantity (EOQ) and materials requirement planning (MRP), have traditionally been complex. This research addresses critical limitations in existing production and inventory management models by addressing recent computational advancements. We propose a comprehensive approach to resolving large-scale industrial engineering optimization problems by integrating high-level programming languages and advanced optimization tools. The study focuses on developing a generic Python-based optimization algorithm using a reference optimization model and Gurobi solver, with primary contributions including: (i) systematic exploration of optimization methods in industrial engineering; (ii) development of a flexible, scalable optimization approach; (iii) demonstration of computational techniques' potential in solving complex production planning challenges. By bridging theoretical optimization models with practical implementation, this research offers a cost-effective solution that extends beyond traditional limitations of economic order quantity and production lot sizing methodologies.