A robust possibilistic programming approach for pharmaceutical supply chain network design

• Developing a bi-objective MILP for a pharmaceutical supply chain network design problem.
• Adopting a robust possibilistic programming approach to handle epistemic uncertainty of uncertain parameters.
• Validating the model using a real case study and providing managerial insights.

In this paper, a bi-objective mixed integer linear programming (BOMILP) model is developed for a pharmaceutical supply chain network design (PSCND) problem. The model helps to make several decisions about the strategic issues such as opening of pharmaceutical manufacturing centers and main/local distribution centers along with optimal material flows over a mid-term planning horizon as the tactical decisions. It aims to concurrently minimize the total costs and unfulfilled demands as the first and second objective functions. Since the critical parameters are tainted with great degree of epistemic uncertainty, a robust possibilistic programming approach is used to handle uncertain parameters. In order to verify and analyze the proposed model, it is tested on a real case study and managerial insights are provided.