A Robust Framework for Integrating Supply Chain Network Design and Assembly Line Balancing Under Uncertainty

This paper presents a mixed-integer linear programming model, integration of designing supply chain network with assembly line balancing. The model simultaneously addresses strategic and tactical decision-making, optimizing supply chain performance while managing costs and risks. It incorporates Conditional Value at Risk to account for demand uncertainties, ensuring resilience against worst-case scenarios. The supply chain configuration includes suppliers, manufacturers, assemblers, and collection centers, with tasks assigned to straight assembly lines, respecting precedence relationships and capacity constraints. Computational results demonstrate the model’s effectiveness in minimizing total costs and achieving robust performance across multiple demand scenarios. Sensitivity analyses highlight the tradeoffs between cost efficiency and risk aversion, providing decision-makers with valuable insights into balancing resilience and operational efficiency.