An uncertain sustainable supply chain network design for regulating greenhouse gas emission and supply chain cost

The increasing global concern for sustainability in supply chain management is driven by stricter government regulations addressing environmental pollution and social injustice. This has led to a growing emphasis on integrating sustainability into supply chain practices. However, there is limited research on incorporating all three dimensions of sustainability (economic, environmental, and social) into supply chain management. This study presents a mixed-integer linear programming model for designing an uncertain supply chain network design that aims to minimize overall costs (establishment, production, and transportation/routing costs) while considering carbon emissions and a few social factors simultaneously. The study considers sustainable aspects of decision-making process and utilizes chance-constrained programming to address uncertainties. The proposed model attempts to maintain balanced flow levels across all stages of the network, optimizing the utilization of raw materials and production. The proposed optimization model is a cost minimization model that also tries to minimize greenhouse gas emissions throughout the entire network. A greedy based heuristic is provided for dealing with larger instances of the given decision making problem. Additionally, sensitivity analysis has also been carried out to explore the impact of various parameters involved.