Optimizing Four-Stage Supply Chain Network Design Considering Environmental Aspects: A Case Of Lead-Acid Battery Of Pinaco Company

Abstract

This thesis presents a novel multi-objective optimization model for the lead acid supply chain, focusing on cost, environmental impact, and social benefits. The model utilizes Mixed-Integer Linear Programming (MILP) techniques and incorporates weights to quantify the importance of each objective, enabling decision-makers to strike a balance between competing goals. To ensure the model's robustness and validity, it is validated using both CPLEX and Python implementations. Four key stages of the supply chain are identified: the warehouse, suppliers, retailers, and manufacturers. The model provides insights into optimizing resource allocation and enhancing overall supply chain performance by looking at each stage's activities and interdependencies. Furthermore, the thesis conducts sensitivity analysis by subjecting the model to demand variations of 10% and 20%. This analysis helps assess the model's responsiveness to changing conditions and provides valuable insights into its adaptability in real-world scenarios. The study's findings contribute to sustainable supply chain management in the lead acid industry, enabling decision-makers to make well-informed decisions that consider economic, environmental, and social aspects. Ultimately, this research aims to foster efficient and responsible practices within the hazardous supply chain, promoting a more sustainable and socially responsible business environment.