A multi-objective stochastic optimization model for pharmaceutical supply chain management based on time and cost

This paper presents a multi-objective stochastic optimization model for pharmaceutical supply chain management (PSCM), focusing on minimizing both time and cost. By considering these objectives and uncertainties simultaneously, the model aims to provide robust and efficient supply chain (SC) strategies for pharmaceutical companies. In this study, a bi-objective mixed-integer linear programming (BOMILP) model is developed for a pharmaceutical supply chain network design (PSCND) problem. The model supports various strategic decisions, such as opening pharmaceutical production centers and main or local distribution centers, as well as determining optimal material flows over a medium-term planning horizon and making tactical decisions. It aims to minimize total costs and flow constraints as the first and second objective functions, respectively. To verify and analyze the proposed model, it is tested on a real case study.