A supply chain model and an application to reduce total cost and carbon emission

In this thesis study, a mathematical model has been developed to simultaneously minimize costs and carbon emissions in the supply chain management processes of a textile company. In the current operational structure of the company, raw material procurement, production planning, inventory management, and distribution processes are carried out independently. This fragmented approach leads to high costs and adverse environmental impacts. The main objective of this study is to minimize total costs and the carbon footprint by jointly considering procurement, production, inventory, and distribution decisions. For this purpose, a Mixed-Integer Linear Programming (MILP) model has been developed. The proposed model incorporates decision variables such as raw material purchasing quantities, product production plans, shipment decisions to meet customer demand, and inventory levels, and it generates solutions under constraints including production capacity, warehouse capacity, safety stock requirements, maximum inventory levels, and customer storage capacity. The model has been solved using Python and the Gurobi optimizer and tested with a realistic dataset. The results indicate that the proposed model effectively reduces both total supply chain costs and carbon emissions, offering a more sustainable and flexible planning approach. The aim of this study is to provide a mathematical model that helps companies simultaneously optimize their economic performance and environmental sustainability objectives. In addition, the study seeks to contribute to the literature on supply chain models that explicitly consider carbon footprint.