Pharmaceutical logistics network planning considering low-carbon policy and demand uncertainty

Abstract

Reducing carbon emissions has become a critical priority in the global effort to combat climate change. This study examined a pharmaceutical logistics network planning problem under drug demand uncertainty within the framework of a carbon cap-and-trade policy. An ambiguity set for medical demand is constructed using historical pharmaceutical order data to account for uncertainty. The problem is then formulated as a two-stage distributionally robust optimization model, with the first stage addressing facility location decisions and the second stage focusing on transportation strategies. A decomposition-based method was developed to solve this model by leveraging the structure of the proposed formulation. Numerical experiments demonstrated the practicality and effectiveness of the proposed models and solution approach. The results show that redesigning the logistics network and leveraging rail transit can achieve reductions of 14.71 % in total costs and 40.75 % in carbon emissions compared to the current case. The analysis also revealed that logistics network configurations and transportation strategies are highly sensitive to carbon pricing. Therefore, governments should enhance carbon emission oversight and stabilize carbon market prices to ensure the effective implementation of carbon cap-and-trade policies.