Optimization of Supply Chain Networks

This thesis develops an optimization model for a multi-echelon supply chain network, incorporating a single production plant, multiple warehouses, distribution centers, and customer zones. The model is designed to optimize key decision variables such as facility establishment, route selection, and product flow across three products, three time periods, and four demand scenarios. The objective is to minimize total costs, including infrastructure, handling, transportation, and inventory holding, while ensuring efficient resource allocation. Constraints are carefully integrated to maintain demand fulfillment, capacity limits, and optimal inventory management. The solution was implemented using Python in the PyCharm development environment, applying advanced computational techniques to derive optimal results. The data used in this study was sourced from a real-world company, ensuring practical relevance and applicability. The findings offer valuable insights for supply chain decision-makers, supporting strategic infrastructure placement, cost-effective logistics planning, and improved adaptability to fluctuating demand conditions. This research contributes to enhancing supply chain resilience and operational efficiency in a dynamic market environment.