| dc.description.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. | en_US |
